Int J Adv Manuf TechnolDOI 10.1007/s00170-005-0340-2

ORIGINAL ARTICLE

C. Sendil Kumar . R. Panneerselvam

Literature review of JIT-KANBAN system

Received: 9 February 2005 / Accepted: 9 September 2005 / Published online: 22 March 2006# Springer-Verlag London Limited 2006

Abstract In this paper, JIT (Just-In-Time)-KANBANliterature survey was carried out and presented. Theintroductory section deals with the philosophy of JIT, andthe concept involved in the push and pull system. Theblocking mechanisms in the kanban system are alsodiscussed elaborately. Besides these sections, the impor-tance of measure of performance (MOP) and the applica-tion of the same with respect to JIT-KANBAN arepresented. The recent trends in the JIT-KANBAN arediscussed under the heading “Special cases”. In this review,100 state-of-art research papers have been surveyed. Thedirections for the future works are also presented.

Keywords JIT . KANBAN . Blocking Mechanisms .CONWIP . Measure of performances (MOP) . Simulation

1 Introduction

Just -In-Time (JIT) manufacturing system was developedby Taiichi Ohno which is called Japanese “Toyotaproduction system”. JIT manufacturing system has theprimary goal of continuously reducing and ultimatelyeliminating all forms of wastes (Brown et al. [5], Ohno[54], Sugimori et al. [82]). Based on this principle,Japanese companies are operating with very low level ofinventory and realizing exceptionally high level of qualityand productivity (Richard J. Tersine [62], James H. Greene[30]). JIT emphasizes “zero concept” which meansachievement of the goals of zero defects, zero queues,

zero inventories, zero breakdown and so on. It ensures thesupply of right parts in right quantity in the right place andat the right time. Hence, the old system of materialacquisition and, buyer and seller relationships are changedto new revolutionary concepts (Womack et al. [91],Womack and Jones [92], Markey et al. [45]). Similarly,JIT becomes an inevitable system at plant level, whichintegrates the cellular manufacturing, flexible manufactur-ing, computer integrated manufacturing and Robotics(Schonberger [63], Golhar [12]).

Due to the technological advancement, the conventionalmethod of push production system linked with MaterialRequirement Planning (MRP) was changed to pull type JITproduction system to meet out the global competition,where the work-in-process (WIP) can be managed andcontrolled more accurately than the push- productionsystem (Mason Paul [46]).

KANBAN system is a new philosophy, which plays asignificant role in the JIT production system. Kanban isbasically a plastic card containing all the informationrequired for production/assembly of a product at each stageand details of its path of completion. The kanban system isa multistage production scheduling and inventory controlsystem. These cards are used to control production flowand inventory. This system facilitates high productionvolume and high capacity utilization with reduced produc-tion time and work-in-process.

The objectives of this paper are as listed below

1) Critical review of JIT literature.2) Segregating the different research articles of JIT.3) Exploring the recent trends in JIT-Kanban system and

deriving directions for future research.

In this paper, the articles are reviewed and an appropriateclassification is presented.The kanban study was madeelaborately, since it acts as a basic communicator and feed-back agent to the JIT system. Push and pull system,principle of operation of kanban cards, Blocking mecha-nism, Toyota’s formula, and the measures of performances(MOP) are also discussed in this paper. The latest trends inJIT-Kanban system are also addressed separately under the

C. Sendil KumarNeyveli Lignite Corporation,Neyveli, India

R. Panneerselvam (*)Department of Management Studies,School of Management, Pondicherry University,Pondicherry 605 014, Indiae-mail: panneer_dms@yahoo.co.in

32:(2007) 393–408

heading “Special cases”. Finally, the directions for futureresearches are presented.

2 Push and pull systems

Push and Pull system are two types of production systems,which operate equally in opposite sense and have their ownmerits and demerits (Monden [50], Villeda Ramiro et al.[89]).

Push system It is a conventional system of production.When a job completes its process in a workstation, then itis pushed to the next workstation where it requires furtherprocessing or storing. In this system, the job has a job cardand the job card is transferred stage by stage according toits sequence. In this method, due to unpredictable changesin demand or production hinder-ness, the job happens todeviate from its schedule and it causes accumulation ofwork-in-process inventory. Hence, inventory plannerspessimistically fix the safety stock level on the higherside. A schematic representation of the push system isshown in Fig. 1. In Fig. 1, WSj is the jth workstation andthe product line consists of n workstations.

Pull system A pull type production system consists of asequence of workstations involving value addition ineach workstation (WS). In the pull system, from thecurrent workstation (j), each job is withdrawn by itssucceeding workstation (j+1). In other words, the job ispulled by the successive workstation instead of beingpushed by its preceding workstation. The flow of partsthroughout the product line is controlled by Kanban Cards(Turbo [87]). In practice, these kanban cards can be either“single-card system” or “two-card system”. Each work-station has an inbound stocking point and an outboundstocking point. The primary advantage of the pull systemis the reduced inventory and hence the associated cost ofinventory reduction. A schematic view of the pull systemwith two workstations and store is shown in Fig. 2.

A kanban system operates only with single card iscalled production order kanban (POK) (J. Berkley [4],Sarathapreeyadarishini et al. [78]). If the distance betweenthe consecutive workstations is very short, a single buffermode is made available between the workstations. Thisbuffer mode acts as both outbound buffer for the currentworkstation j and inbound buffer for the succeedingworkstation j+1, respectively. A schematic diagram of asingle-card system is shown in Fig. 3. In the two-cardsystem, where the distance between the two consecutivework stations are more, each work station will haveseparate inbound buffer and outbound buffer (Kimura O. etal. [36], Hemamalini et al. [21]) and the cards are called asProduction Order Kanban (POK) and Withdrawal Kanban

(WK), respectively. A schematic diagram of a two-cardsystem is shown in Fig. 4.

2.1 Operation of two-card kanban system

The two-card kanban pull system which works inthe Assembly/Manufacturing line is elaborated byPanneerselvam [56], Edward J. Hay [17], Kimura andTerada [36], Hunglin Wang et al. [25] and Hemamalini etal. [21] and Shahabudeen et al. [76]. Basically it has plasticcards, which give information about the parts and alsothings to be done. The production order kanban (POK) is aproduction order, which instructs the preceding work-station to produce the required number of units. Thewithdrawal kanban (WK) gives the message to thesucceeding process about the number of units it shouldwithdraw.

The simple steps adopted in kanban system are asfollows

1) The container of the succeeding workstation j+1 ismoved to the preceding workstation j with thewithdrawal kanban (WK) and placed it in its outputbuffer.

2)a) Consequently it pulls the parts from output buffer of

the workstation j and detach the production orderkanban (POK) attached to those parts and then placesthe POK in the POK-post of the workstation j.

b) Work station j starts its production as per theproduction order in its POK post.

3) The container along with the parts and WK movesagain to its succeeding workstation j+1. Then itdelivers the parts to the input buffer of the workstationj+1 and places the WK to the WK-post of theworkstation j+1.

oooWS1 WS2 STORE WSj WSno o o

Fig. 1 Push system

Request for items Request for items

Items movement Items movement

WS 1 STORE WS 2

Fig. 2 Pull system

Only card movement

Card + Parts movement

BUFEER

WSj WSj+1

POK POST

Fig. 3 Schematic diagram of a single card system

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4) When the parts in the containers of the workstation j+1are fully used, then the steps from 1 to 3 are repeated.

3 Blocking mechanisms

Each workstation of a production/assembly line requiressufficient space for storing parts in its output buffers. Whenthe buffer capacity of a workstation is fully occupied, nofurther storage is possible. Because of this fact, theworkstation can not release the parts and hence, it cannot process components. This condition is called “Block-ing”. The blockings are categorized according to the typesas presented in Table 1.

3.1 Single card -instantaneous

As discussed earlier, if the workstations are situated closerto each other, the output buffer of the workstation j and theinput buffer of the workstation j+1 are one and the same.Under such situation, a single card instantaneous kanban isused. Berkley [4] and Sharadhapreeyadarishini et al. [77]have discussed the blocking mechanism of single card typein detail.

3.1.1 Blocking due to part type

This type of blocking occurs due to restriction in thenumber of parts (containers) that can be stored in the bufferbetween workstation j and the workstation j+1. Theworkstation j will not process the particular part p, sincethere is no reserved space in the buffer storage for theparticular part type.

Let Q(p, j, j+1) be the maximum number of units(container) of part type p that can be stored in the bufferstorage between the workstation j and the workstation j+1.Then the workstation j can process the p type parts, only if theactual number of units (container) of the part type p in thebuffer storage less than Q(p, j, j+1); otherwise, the work-station j is blocked due to part type p alone. The workstationcan process any other part type provided that workstation isnot blocked with respect to that part type.

3.1.2 Blocking due to queue size

This type of blocking occurs due to restriction in the totalnumber of containers of all part types in the buffer betweenworkstation j and the workstation j+1. The workstation jwill not process any part type if there is no space in thebuffer storage between the workstation j and the work-station j+1, irrespective of part type and container.

Let Q (j, j+1) be the maximum number of containersirrespective of the part types that can be stored in the bufferstorage between the workstation j and the workstation j+1.Then the workstation j can process part types, only if theactual total number of containers in the storage between theworkstation j and the workstation j+1 is less than Q(j, j+1);Otherwise, the work station j is said to be blocked due tothe queue size constraint.

3.1.3 Dual blocking mechanism

If both the above blocking mechanisms operate simulta-neously, then it is called Dual blocking mechanism.

The work station j is said to be blocked if the actualnumber of units (containers) of the part p in the bufferstorage between the workstation j and the workstation j+1is equal to Q(p, j, j+1) and the actual total number ofcontainers in the buffer storage between the workstation jand the workstation j+1 is equal to Q(j, j+1).

Subsequently, when a container of the part type p istaken by workstation j+1, then the blocking is released andthe workstation j can start processing the part p. If the workstation j+1 takes the container of any part other than that ofp, then the work station j is still blocked with respect to partp and it is not blocked with respect to other part types.

3.2 Two card- non-instantaneous

If the distance between consecutive workstations is more,there will be independent input and output buffer points for

Table 1 Categories of blocking mechanisms

Single Card -Instantaneous Two Card - Non Instantaneous

1) Blocking due to part-type. 1) Blocking due to part-type.2) Blocking due to queue size. 2) Blocking due to queue size.3) Dual blocking mechanism. 3) Dual blocking mechanism.

Blocking mechanism Operativeon Material Handling.4) Blocking due to part-type.5) Blocking due to queue size.6) Dual blocking mechanism.

POK WK

WK+ Parts

Output Buffer Input Buffer

of of

Workstation j+1

WK

WS j

WSj+1

POK POST

( j )

WK POST (j+1)

Workstation j

Fig. 4 Schematic diagram of two card system

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each workstation. In this system, the blocking can occurdue to stagnation of parts in the output buffer of thatworkstation. Berkley J. [4] et al. [21] have studied this typeof blocking.

3.2.1 Blocking due to part type

This type of blocking occurs due to restriction in thenumber of parts (containers) that can be stored in the outputbuffer of the workstation j. The workstation j can notprocess the particular part p, since there is no reservedspace for the part type p in the output buffer of theworkstation j.

Let Q(p, j) be the maximum number of units (containers)of the part type p that can be stored in the output bufferstorage of workstation j. Then the workstation j can processthe parts of the part type p, only if the actual number ofunits (containers) of p in the output buffer storage of theworkstation j is less than Q(p, j); otherwise, the workstationj is blocked due to the part type p alone. The workstation jcan process parts of any other part type provided that theworkstation is not blocked with respect to that part type.

3.2.2 Blocking due to queue size

This type of blocking occurs due to restriction in the totalnumber of containers of all part types in the output buffer ofthe workstation j. The workstation j will not process any ofthe part types since there is no space in the output bufferstorage of the workstation j, irrespective of part type andcontainer.

Let Q(j) denotes the maximum number of containersirrespective of part type that can be stored in the outputbuffer storage of the workstation j.

Then the workstation j can process parts only if theactual total number of containers in the output buffer of theworkstation j is less than Q(j); otherwise, the workstation jis said to be blocked due to the queue size constraint.

3.2.3 Dual blocking mechanism

If both of the above blocking mechanisms operate simul-taneously, then it is called dual blocking mechanism.

The workstation j is said to be blocked if the actualnumber of units (containers) of part type p in the outputbuffer of workstation j is equal to Q(p, j) and the actual totalnumber of containers in the output buffer of workstation j isequal to Q(j).

Subsequently, when a container of part type p is taken tothe input buffer of the workstation j+1, the blocking will bereleased and the workstation j can start processing the parttype p. If the input buffer of workstation j+1 takes acontainer of parts other than the part p, then the workstationj is still blocked with respect to the part type p.

3.3 Blocking mechanisms operative on materialhandling

Material handling operation between the workstation j andthe workstation j+1 can be blocked due to part type, queuesize or both. This is similar to the above types but theblocking is due to Material Handling (MH) between outputbuffer of the workstation j and the input buffer of theworkstation j+1. This was studied by Berkley J. [4] andHemamalini et al. [21].

3.3.1 Blocking mechanism due to part type

This type of blocking occurs due to restriction in thenumber of parts (containers) that can be stored in the inputbuffer of the workstation j+1.

Let M(p, j+1) denotes the maximum number of units(containers) of part type p that can be stored in the inputbuffer storage of the workstation j+1.

Then, materials handling is permitted from the outputbuffer of work station j to the input buffer of workstationj+1, if the actual number of units (containers) of part p inthe input buffer of work station j+1 is less than M(p, j+1).

3.3.2 Blocking mechanism due to queue size

This type of blocking occurs due to restriction in the totalnumber of containers of all part types that can be stored inthe input buffer of the workstation j+1.

Let M(j+1) be the maximum number of containersirrespective of part types that can be stored in the inputbuffer storage of workstation j+1. Then, the materialhandling is permitted from output buffer of the workstationj to the input buffer of workstation j+1 only, if the actualtotal number of containers in the input buffer of workstation j+1 is less than M(j+1).

3.3.3 Dual blocking mechanism

If both blocking mechanisms discussed in sections 3.3.1and 3.3.2 operate simultaneously then it is called dualblocking mechanism. The material handling operation issaid to be blocked, if the actual number of units (contain-ers) of part type p in the input buffer of the workstation j+1is equal to M(p, j+1) and the actual total number ofcontainers in the input buffer of the workstation j+1 isequal to M(j+1). Subsequently, when a container of the parttype p is taken from the input buffer of workstation j+1, theblocking will be released and the material handling starts toclear the parts from the output buffer of the work station j.Now, the workstation j can start processing the part p. If theworkstation j+1 takes a container of parts other than that ofpart p from the input buffer of the workstation j+1, then thematerial handling is not possible for the part p. So theworkstation j is continued to be in blocked state with

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respect to the part p. However, the workstation j is notblocked with respect to other part types.

4 Toyota’s kanban formula

The formula used by Toyota Motor Company to determinethe number of kanbans is called Toyota formula. (Berkley[4], Chan [6], Henry et al. [10], Hunglin Wang et al. [25],Ohno et al. [53], Monden Y. [50], Philipoom et al. [60] andYavuz et al. [98]). The Toyota’s kanban formula ispresented below.

K � DL 1þ αð ÞC

where,K is the number of kanbans,D is the demand per unit time,L is the lead-time,α is the safety factor andC is the container capacity

From these literatures, it was noted that the lead-timeincludes waiting time, processing time, conveyance timeand kanban collecting time. The safety stock serves as abuffer against variations in both supply and demand. Henryet al. [10] has suggested some practical values for thevariables C and α. The value of C is limited to a maximumof 10% of demand and α is a policy variable, which isdecided by the management up to 10% of the demand. Thevariable K is the number of kanbans, which is related to thestock. If the value of K increases, the stock of the parts alsoincreases. As a result, idle stock occurs. Similarly, if thevalue of K decreases, the stock of parts also decreases andshortage occurs. Hence, the JIT production system appliestrade-off between the above parameters to find the optimumnumber of kanbans. Many researches have been carried outto find the optimum number of kanbans using differentmethodologies and tools such as simulation, queuingmodels, mathematical models, Artificial Intelligent ap-proach and so on. From the Toyota’s empirical equation,one can find the number of kanbans required for the system.

5 Measure of performance (MOP)

For any system, the efficiency is measured through afunction of related parameters/ factors. Hence these factorsmust obviously establish close relationship with thefocused problem. These factors individually or jointly repre-sent a performance. Blair Berkly J. [4] has given a note onworkstation performance in kanban controlled shops interms of average inventories, quality and the ability to meetthe demands. Our study reveals that various researchershave used thirteen factors and they are shown in Table 2.

From Table 2, it is inferred that the average work-in-process (WIP), average flow time, mean cumulativethroughput rate and weighted earliness of the job are

frequently used as performance measures. Some importantdefinitions for the factors, which are used in differentMOPs by various researchers, are discussed below.

Yavuz and Satir [98] have used seven factors in theirstudy, which are as presented below.

1) Mean Cumulative Throughput Rate: It is the ratio oftotal satisfied demand to the total generated demand.

2) Mean Total Production Lead Time: It is the amount oftime spent by a job from entering the system to untilcompletion of all operations, averaged over allcompleted job.

3) Mean Total Demand Satisfaction Lead Time: It is thetime interval between arrival of the demand andsatisfaction of the demand.

4) Mean Utilization of Line: It is the mean utilization ofthe last station in the line.

5) Mean Setup/Run Time Ratio of Line: It is the ratiobetween the setup time and the run time of last station.

6) Mean Total WIP Length: It is the mean of all in-process-inventory levels for the products excludingfinished goods (FG).

7) Mean Total Waiting Time: It is the waiting time of allproducts in all processes and finished goods inventory(FGI).

A general purpose analytical model to evaluate theperformance of multistage kanban controlled productionsystem was developed by Di Mascolo et al. [15]. Theperformance measures used by them are percentage ofdemand for back-order, average waiting time of back-order and average work-in-process.

Table 2 Factors used by various researchers

Sl.No

Factors usedfor MOP

The reference numbersof research articles whichuse the MOP

1 Average WIP [2, 6, 15, 48, 76, 79, 89, 90, 98]2 Demand [6, 15, 76]3 Fill Rate [6]4 Average kanban

waiting/queue time[74, 77, 98]

5 Average Flow(production lead) time

[2, 6, 22, 31, 51, 61, 76, 77, 98]

6 Average setup/processtime ratio

[98]

7 Average input/outputinventory

[79, 89]

8 Mean cumulativethroughput rate

[48, 74, 88, 90, 98]

9 Mean line utilization [48, 89, 98]10 Mean demand

satisfaction lead time[98]

11 Mean staging delay of job [49, 51]12 Mean Tardiness [2, 22, 51, 61, 79]13 Weighted earliness of the

job[22, 61]

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A simulation experiment to evaluate the relative effective-ness of various rescheduling policies in capacity-constrained,JIT make-to-stock production environment is examined byKern et al. [34]. Three performance measures analyzed bythem are average finished goods inventory, total units of saleslost, and measure of schedule instability. Jing-Wen Li [31]has measured three factors for shop performance which areaverage work-in-process (WIP) inventory, average flow timeand average set up time to processing time ratio (ASOTR),which is the ratio of total amount of time spent for setting upmachines to the total amount of time spent for processingparts averaged over all machines. Uday S. Karmarker [88]used throughput rate for total work performance. In anotherstudy, the priority rule assignment was checked by thefollowing factors by Nabil R. Adam et al. [51].

1) The lead time of a job2) The flow time of the job3) The staging delay of a job4) Mean Tardiness

Hemamalini et al. [22] considered the objective functionto minimize the sum of weighted flow time, weightedearliness of jobs and weighted tardiness of containers.Shahabudeen et al. [76] used an universal test which maybe suited for the MOP in any JIT system, which arepercentage zero demand (PZD), mean lead time (MLT) andmean total WIP (MTW) as explained below.

1) Percentage zero demand: It is the percentage of totaldemand immediately satisfied to the total generateddemand.

2) Mean lead time: It is the sum of the waiting time,processing time and moving time averaged per station.It is also called as mean flow time.

3) Mean total WIP: It is the average number of kanbanswaiting for each part type at each workstation.

Here, PZD is a maximization measure and, MLT andMTWare the minimization measures and hence the sum ofthe objective MOPs is changed as Zmax (a1PZD +a2 RMLT+a3 RMTW), where a1, a2 and a3 are weights of therespective measures and, RMLT and RMTW are modifiedvalues of MLT and MTW, respectively.

Chan F.T.S. [6] has done a work on how the MOPchanges in different production systems, while increasingthe kanban size. The measures of performance taken byhim are as listed below

1) Unsatisfied order, which is the difference between theactual number of unit produced and the level ofdemand.

2) Manufacturing lead-time, which is the time betweenthe customer order and the completion of order.

3) In-process-inventory is the total number of work-in-process (WIP) inventory in units excluding finishedgoods inventory.

4) Fill rate is the percentage of demand satisfied.

The results of his study as a function of the kanban sizeare shown in Table 3.

6 Literature review

Golhar et al. [12] have classified the JIT literature aselimination of waste, employee participation, supplierparticipation and total quality control.

A similar work was done by Berkly [4] for kanbanproduction process. He has selected 24 elements in thekanban production system as operational design factors.

In this section, the different topics associated with “JIT-KANBAN” studied by various researchers have beengrouped and presented as shown in Fig. 5. The Table 4shows the reference numbers of the articles with respect tothe classifications shown in Fig. 5.

Obviously, most of the researchers were focusing on thedetermination of number of kanbans and determiningcorresponding solutions by using suitable models andtools. Some authors have developed simulations model andmeta-heuristics like, genetic algorithm (GA), tabu search(TS), and simulated annealing (SA) for JIT-Kanban forbetter solutions.

The Table 5 shows the number of articles dealt indifferent periods. From, Table 5, it is clear that, during lasttwo 5 years period (1996-2000 & 2001-2005), the numberof researches are more. Further, more researches have beendone in empirical theory, flow shop, simulation, variabilityand its effects, CONWIP and special cases. Manyresearchers have worked in JIT system with differentobjectives. Here, the authors have grouped some important

Table 3 Results of the study by Chan [6]

MOP Pull(Singleproduct)

Hybrid(Singleproduct)

Hybrid(Multi-product)

1) Fill rate Decrease Decrease Increase2) In-process-inventory

Increase Increase Increase

3) Manufacturinglead time

Increase Increase Decrease

JIT

SPECIAL CASES

FLOW SHOP

SCMKANBAN

VARIABILITY &ITS EFFECTS

CONWIP

ASSEMBLYLINE

BATCH

POLCA

DIFFERENT MODELS MATHEMATICAL QUEUING MARKOVIANS SIMULATIONS COST MINIMIZATION

Fig. 5 Flowchart showing the classification of literature review

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objectives of the researches into six headings as shown inTable 6. From Table 6, it is clear that the followingobjectives attracted more researchers.

– Design of kanban system– Performance behaviour– Sequencing and scheduling

6.1 Empirical theory

In the paper by Monden Y. [50], a comprehensivepresentation of Toyota production system is given. Asuccessful kanban system will drastically reduce thethroughput time and lead time (Philipoom et al. [59]).

Karmarker and Kekre [33] have concluded from theirstudies that the reduction in container size and increase innumber of kanbans lead to better results. Many researcherswere interested in finding the optimal number of kanbans.The Toyota formula is very much useful in determining theoptimal number of kanbans.

Co Henry et al. [10] used the Toyota formula and alsoinvestigated the safety stock allocations in an uncertaindynamic environment. A similar work was considered bySarkar et al. [71] to find number of kanbans between twoadjacent workstations. Yale T. Herer et al. [95] presented astudy for kanban system, CONWIP and buffered produc-tion lines. In this study, they incorporated a non-integralapproach using simulation. The use of non-integralapproach helps production planners to obtain discretenumber of kanbans.

Woolsey et al. [93] have developed a simple spreadsheetoptimization program to determine the correspondingnumber of kanbans with respect to user-defined safetystock levels and other values. It gives a close-form of

Table 4 Details of classification of review articles

Area of Research Reference numbers of related Articles

JIT [4, 12]Kanban-Empirical theory [10, 33, 50, 59, 71, 93, 95]Flow shop [7, 22, 58, 61, 77, 78]Assembly line [16, 89, 94]Batch ProductionSystem

[35, 86]

Modeling Approach:Mathematical

[3, 36]

Queuing [73, 99]Marko-chain [14, 28, 52, 90]Simulation [1, 9, 13, 19, 26, 66, 67, 74, 75]Cost minimization [53, 68, 72]Variability and its effects [7, 24, 48, 89, 98]CONWIP [8, 44, 55, 69, 70, 79, 96]POLCA [69]SCM [18, 29, 47, 80]Special Cases [6, 38, 40, 41, 43, 60, 69, 84, 85, 97, 100]

Table 6 Objective based classification and their references

Classification Reference numbers of articles

A. Principles ofJIT-Kanban system

[21, 50, 55, 59, 96]

B. Operating Factors [4, 12, 80]C. Design of KanbanSystem

[1, 10, 13, 19, 26, 52, 53, 66, 71, 74, 75,93]

D. Performancebehaviour

[7, 24, 33, 36, 48, 58, 73, 81, 89, 90, 98,99]

E. Sequencing& Scheduling

[18, 22, 57, 58, 61, 77, 78, 94]

F. Inventory/BufferControl

[3, 68, 86]

Table 5 Details of researches in different periods

Area of Research 1980-1985 1986-1990 1991-1995 1996-2000 2001-2005 Total

JIT 2 2Kanban- Empirical theory 1 2 3 6Flow shop 1 4 1 6Assembly line 1 1 1 3Batch 1 1 2Modelling Approach: Mathematical 1 1 2Queueing 2 2Markovians 1 2 1 4Simulation 1 1 5 2 9Cost minimization 1 1 1 3Variability and its effects 1 1 3 5CONWIP 2 3 2 7POLCA 1 1SCM 4 4Special cases 1 1 2 7 11Total 3 10 11 23 20 67

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solution to the problem. This means that an answer for anyproblem size may be instantaneously obtained.

6.1.1 Flow shop

Kanban system is widely implemented in repetitivemanufacturing environment. For a single card operationalsystem, Sharadhapriyadarishini et al. [77] have developedtwo heuristics and proved that these are more efficient.Saradhapriyadarishini et al. [78] have proposed a recursiveequation for scheduling the single card kanban system withdual blocking. They proposed a heuristic with twinobjectives of minimizing the sum of total weighted timeof containers and weighted flow time of part-types.Rajendran [61] has done a work on two card flow shopscheduling with n part-types. In this paper, mathematicalmodels for time tabling of containers for different problemshave been formulated. Then, a heuristic was developed tominimize the sum of weighted flow time, weighted earliness,and weighted tardiness of containers. Hemamalini et al. [22]have done similar work. In this work, the heuristic developedis simulated annealing algorithm. This is compared withrandom search method. In these papers, the comparisons aredone only based on mean relative percentage increase.Instead of this approach, comparisons based on completeANOVA experiments would provide reliable inference.

Peter Brucker et al. [58] have carried out research onflow shop problem with a buffer of limited capacitybetween two adjacent machines. After finishing theprocessing of a job on a machine, either the job is to beprocessed on the following machine or it is to be stored inthe buffer between these machines. If the buffer iscompletely occupied, the job has to wait on its currentmachine but blocks this machine for other jobs. In thispaper, they determined a feasible schedule to minimize themakespan using tabu search. The results of the problemusing tabu search were compared with that of benchmarkinstances. The comparisons are done only based on relativeimprovements. Instead of this approach, comparisonsbased on complete ANOVA experiments would providereliable inference.

6.1.2 Assembly line

Assembly lines are similar to the flow shops in whichassembly of parts are carried out in a line sequence. In amulti product assembly line, the sequencing of the jobs is achallenging task. Drexl et al. [16] considered an assemblyline sequencing mixed model problem. It is a combinatorialproblem. They formulated this combinational problem asinteger programming model. This model can be used onlyfor small size problems due to the limitations of operationsresearch software with respect to handling the number ofvariables and constraints, which are present in the integer-programming model. Xiaobo et al. [94] have consideredsimilar work on mixed model assembly line sequencingproblem with conveyor stoppages. They proposed branch

and bound algorithm, and simulated annealing algorithmfor finding the optimal solution and sub-optimal solution ofthe mixed-model sequencing problem, respectively tominimize the total conveyor stoppage time. The branch-and-bound method was devoted to find the optimalsolution of small-sized problems, whereas the simulatedannealing method was used to cope with large-scaleproblems to obtain a good sub-optimal solution. Future,research on simulated annealing applied to this problemcan be directed to establish a better seed generationalgorithm. However, the practitioner should spend con-siderable time in fixing the parameter called temperature(T) in the simulated annealing algorithm by trail and errormethod before actually solving the problem.

6.1.3 Batch production system

In a batch production system, the switching over from oneproduct to other product depends on many factors such asstock reaching to the threshold level, different priorityschemes, economical setups, etc. Tafur Altiok et al. [86]have dealt this issue differently for the pull typemanufacturing system with multi product types. In thispaper, they developed an iterative procedure to approxi-mately compute the average inventory level of eachproduct as finished goods using different priority schemes.In this paper, the demand arrival process is assumed to bea poisson distribution and processing times and the set-uptimes are arbitrarily distributed. But, in practice, theprocessing times may follow other distributions, viz.,normal, uniform, exponential, etc. which are not experi-mented in this paper. Khan et al. [35] addressed theproblem of manufacturing system that procures rawmaterials from vendors in lot and convert them intofinished products. They estimated production batch sizesfor JIT delivery system and designed a JIT raw materialsupply system. A simple algorithm was developed tocompute the batch sizes for both manufacturing and rawmaterial purchasing policies.

6.2 Modeling approach

Modelling approach aims to obtain the optimal solution.This subsection reviews different modeling approaches.

6.2.1 Mathematical model

Kimera and Terada [36] have developed a mathematicalmodel in the area of kanban system. They have given abasic balance equation for multi stage systems, whichshows how the fluctuation of final demand influences thefluctuation of production and inventory volumes. Bitranand Chang [3] have designed an optimization model for thekanban system. The model is intended for a deterministicmulti-stage capacitated assembly-type production setting.In this paper, a non-linear model developed by them is

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converted into a linear model with deterministic demand.This deterministic model is designed to find the choice ofthe number of kanbans to be used at each stage of a givenproblem and to control the level of inventory. But thisanalysis does not include uncertainties directly. Hence, theutility of this model is very much limited.

6.2.2 Queuing model

Seki et al. [73] have designed a single-stage kanban systemwith poisson demand arrivals. The system is formulated asa queuing system under piecewise constant load, and anumerical method by transient solutions of the queue isapplied. This method, which shows the transient behaviorof the kanban system, gives a better result. Yoichi Seki etal. [99] did similar work on the single stage kanban systemwith poisson demand and erlang production times. Theobjective of this work is to determine the number ofkanbans, when a change of load to the system is planned.They mainly proposed a numerical method by transientsolutions of the queueing system which was developedunder piecewise constant load. This method also showsthat the transient behavior of the kanban system operatesbetter with other parameters. In this paper, the loaddistribution is assumed to be piecewise linear. Instead, itcan be assumed as a continuous distribution and thecorresponding results using simulation can be comparedwith `the results of this paper.

6.2.3 Markovians model

Vito Albino et al. [90] studied a model of kanban controlledmanufacturing system based on Markovian assumption.An approximate approach was developed to solve themodel, which permits reliable evaluation of performance interms of throughput time and work-in-process (WIP).Further, they validated the results using discrete-eventsimulation applied to their problem. It was observed thatthe results of the approximation approach did not deviatemuch from that of the simulation approach. The errors werealways within 5% even for moderate size problems with 20stages. The comparisons made in this paper were based onabsolute value of percentage relative errors. Instead of thisapproach, they should have done comparisons through acarefully designed ANOVA experiments. Nori and Sarkar[52] have modeled the kanban system using Markov-chainto determine the optimum number of kanbans betweenadjacent workstations.

Deleersnyder et al. [14] have modeled a blockingsituation in the queues of the kanban system using discretetime Markovian chain to study the effect of number ofkanbans, machine reliability, processing time and demandvariability. Markham et al. [28] formed a procedure basedrule induction approach for determining the number ofkanbans and other factors in JIT. They applied classifica-tion and regression tree (CART) technique to generate theproduction rule, based on decision trees. This system

approach involves 3 steps methodology, viz., 1) datacollection, 2) formation of decision tree, and 3) interpre-tation of decision tree. This method helps to set kanbanlevels under high demand variability. The results show thatrule induction using CART is a viable solution to theknowledge acquisition bottleneck. Hence, an extendedwork on knowledge acquisition for this domain will be asignificant contribution to literature.

6.2.4 Simulation based studies

There are many simulation softwares available in themarket, such as GPSS, Q-GERT, SLAM-II, SIMAN,SIMSCRIPT, EXTEND, ARENA, and SIMULINK. Sim-ulation uses the attributes/parameters of a problem to arrivethe results. As for as designing of kanban system, a basicsimulation study was done by Davis et al. [13] and Gabrielet al. [19] to determine the number of kanbans. In anotherwork by Rudi De Smet et al. [67], a simulation model wasdeveloped to study the feasibility of plans to produce somesubparts of the product in a kanban-controlled manner todetermine the operational parameters such as number ofkanbans and container size. This feasibility study wascarried out for two situations, namely (1) all subpart typesare produced in a kanban controlled manner and (2) onlythe production of fast-movers on two (out of three)machines is kanban controlled. The result assures that thekanban control is the best method for fast moving parts.

In a kanban control system, the main decisionparameters are the number of kanbans and lot size. Alabaset al. [1] developed three-meta heuristics viz., geneticalgorithm (GA), simulated annealing (SA) and tabu search(TS) coupled with a simulation model to find the optimumnumber of kanbans with the minimum cost. In addition, aneural network metamodel was developed and comparedwith the heuristic procedures in terms of solution accuracy.They found that the tabu search requires less computationalefforts when compared to the other two meta-heuristics andthe neural network meta-model. In a similar work byHurrion R.D. [26], simulation and neural network meta-model have been used for designing the kanban system. Inthis paper, an approximate solution is found using neuralnetwork meta-mdoel and then it is used as the starting pointin simulation to find the optimum number of kanbans of amanufacturing system. Actually, the word “optimum”should have been avoided in his paper, because neitherthe proposed meta-model nor the simulation approach willgive optimal number of kanbans. The optimum number ofkanbans may be called as the minimum number of kanbans.

In this context, an attempt has been made byShahabudeen et al. [75] to set the number of kanbans aswell as lot size at each station using simulated annealingalgorithm. A simulation model with a single-card systemhas been designed and used in the analysis. A bi-criterionobjective function comprising of mean throughput rate andaggregate average kanban queue, has been used forevaluation. In another work of them (Shahabudeen andKrishnaiah [74]), they have set the number of production

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kanbans and withdrawl kanbans at each workstation, andlot size using genetic algorithm (GA). The solution of thegenetic algorithm is found to be better than the randomsearch procedure. They concluded that the genetic algo-rithm gives better solution for the assumed kanban system.

A paper by Royce O. Bowden et al. [66] describes theuse of evolutionary programming (EP) integrated with asimulation model of manufacturing system to determinethe minimum number of kanbans and correspondingproduction trigger values required to meet the demand. Inthis paper, the inference is drawn for each measure, basedon single replication under each solution-technique. Theauthors could have designed a single factor ANOVAexperiment for each measure in which “Solution Tech-nique” as the factor, with desirable number of replicationsto obtain reliable inference of their simulation study.Christos G. Panayioton et al.[9] have developed a simu-lation based algorithm for determining the minimumnumber of kanbans in a serial production system in orderto maximize the throughput rate and minimize work-in-process inventory. The finite perturbation analysis (FPA)technique was used in the simulation and to get sensitivityresults. They have considered single product in theproduction line. But, in most of the cases, productionlines will be manufacturing multi-products. The assump-tions of arbitrary arrival and service process distributionslimit the scope of application of this paper in practice.

6.2.5 Cost minimization model

Ohno et al. [53] proposed an algorithm to determine theoptimal number of kanbans for each of the two kinds ofkanban (production ordering and supplier kanbans) understochastic demand. An algorithm was devised for deter-mining the optimal number of kanbans that minimizes theexpected average cost per period. Since, no safety stock isassumed in this paper, this can be regarded as a procedurefor determining the safety stock also. Sarkar et al.[72]studied a multi stage kanban system for short life-cycleproduct in the market. In this research, the problem is tofind optimally the number of orders for raw-materials,kanbans circulated between workstations, finished goodsshipments to the buyers, and the batch size for eachshipment (lot) with minimum total cost of the inventory. Acost function was developed based on the costs incurred forthe raw materials, the work-in-process and the finishedgoods. The optimal number of raw material orders thatminimizes the total cost is obtained first, which is then usedto find the minimum number of kanbans, finished goodsshipments, and the batch sizes of shipments. This paperdiscusses a stage-wise optimization. Instead, a fullyintegrated approach may be followed. Further, this paperconsiders single product, with constant production rate ateach workstation in a serial production line. So, the workmay be extended for multi-product with varying productionrate at each workstation in an assembly-type production.

During preventive maintenance, a JIT buffer is neededso that the normal operation will not be interrupted. The

optimal JIT buffer level is determined from a cost analysisusing trade-off between the holding cost per unit of timeand the shortage cost per unit time such that their sum isminimized (Salmark et al. [68]).

6.3 Variability and its effects

Mehmet Savsar et al. [48] studied a simulation model toinvestigate the effect of different operational conditions,including kanban withdrawal policies on three perfor-mance measures of JIT, viz., average throughput rate,average station utilization and average work-in-process.Unlike other simulation studies that use exponential ortruncated normal distribution, this model uses Erlang andGama distribution. It is observed that the throughput rate aswell as the average station utilization is significantlyaffected by the variability in processing time and demandintervals. They proposed two types of kanban withdrawalcycles, namely fixed withdrawal policy and variablewithdrawal policy. Under the fixed withdrawal policy, thetime interval between consecutive visits of a part-carrier toa workstation for kanban removal is fixed, but the orderquantity (number of kanbans carried) is variable whereasunder the variable withdrawal policy, the time intervalbetween consecutive visits of a part-carrier to a workstationfor kanban removal is variable, but the order quantity isfixed. As an extension of this work, the effects of differentcombinations of the two kanban withdrawal policies andnumber of kanbans between workstations, on the perfor-mance measures can be compared.

Huang et al. [24] have found that overtime required willbe increased when the variation in processing time isincreased. Also, they emphasized that a kanban systemwould not be effective with high variable processing or setup time. Villeda et al. [89] performed a simulation study fora final assembly consisting of “3 sub-assembly lines and 4stages” repetitive production systems with kanbans. Theyconcluded that improved productivity obtained throughunbalancing the processing time at all workstationsincreases directly with the variability in the final assembly.Chaturvedi and Golhar [7] simulated a kanban based flowproduction line for a product in nine sequentially arrangedworkstations. They observed that the system performancewas worst for exponential processing time distribution andvariability affected station utilization, throughput time andWIP inventory. Yavuz and Satir [98] have studied thesimulation of multi-item, multi-stage flow line operatingunder the JIT philosophy with a two-card kanban tech-nique. The flow line produces four products through fivestations. This study uses partial factorial design forexperimentation. Seven experimental clusters are designed,each composed of at most three factors. The F ratios andthe degrees of freedom of the model are obtained frommulti-variate analysis of variance (MANOVA). They foundthat decrease in lot size reduces mean length and waitingtimes in work-in-process points at all kanbans levels. Anincrease in the uncertainty of demand arrival rates anddemand sizes increases the probability of sudden over-

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loading. An increase in the coefficient of variation inprocessing times brings about higher line utilization and adecrease in throughput rate. The scheduling rules testedin this paper are found to yield no significant differences inthe utilization of line and on the behaviours of work-in-process. Feeder lines may be introduced into the pullsystem configuration, where lines feed the final assemblyline. Further, alternate operating routes for the productsalong the line may be introduced.

6.4 CONWIP

CONWIP is a kanban system working with constant work-in-process. CONWIP is a generalized form of kanban.Like, kanban system, it relies on signals, which could beelectronic and it is equivalent to kanban cards. In aCONWIP system, the cards traverse a circuit that includesthe entire production line. A card is attached to a standardcontainer of parts at the beginning of the line. When thecontainer is used at the end of the line, the card is removedand sent back to the beginning of the line where it waits in acard queue to eventually be attached to another container ofparts.

Oscar Rubiane et al. [55] have reviewed the literaturesand presented the benefits and comparison of the CONWIPsystems. Most of the articles reveal that the CONWIPsystem works more efficiently than the conventionalkanban systems. Yang and Kum Khiong [96] compared 3different systems viz., Single Kanban, Dual Kanban andConwip. The results show that CONWIP consistentlyproduces the shortest mean customer waiting time andlowest total work-in-process. Spearman et al. [79] havestressed that the flexibility of CONWIP system allows it tobe used by any product-line where the utility of kanbansystem is limited. Hence, the superiority of CONWIP pullsystem is an alternative to kanban system. They presenttheoretical arguments and simulation study of CONWIP.

Christelle Duri et al. [8] have analyzed CONWIPsystem, which consists of three stations in series. When afinished part is consumed by a demand, a raw part isreleased immediately and gets processed at each stationsequentially. The processing at each station does notalways meet the requirement of quality. Hence, at the endof processing in a station, the part is checked for qualityand if it is not as per the standard, then it is sent back to thesame station for reprocessing. They proposed an analyticalmethod to evaluate the performance of this kind of system.In this paper, only three stations in series are considered.As an extension, a CONWIP system with generalized,n stations in series may be analyzed.

6.5 SCM

There are number of articles in SCM (Supply ChainManagement). In this present survey, a few JIT-SCMrelated articles are reviewed. In pull production manage-ment systems such as JIT, deliveries must be made on an

as-needed basis only, and production begins only whenrequested. It is supposed to match customer demand, thatis, producing only enough to replenish what the customerhas used or sold.

F. Elizabeth Vergara et al.[18] have dealt the co-ordination between different parts of simple supply chain.Materials should be moved from one supplier to othersupplier as per the JIT. For this, an evolutionary algorithmwas used which identifies the optimal or near optimal,synchronized delivery cycle time and suppliers’ compo-nent sequences for a multi-supplier, multi-componentsimple supply chain. The evolutionary algorithm alsocalculates a synchronized delivery cycle time for the entiresupply chain, the cumulative cost throughout the supplychain, and the cost to each supplier. The results of thisalgorithm were compared with enumeration method andfound that the evolutionary algorithm gives better solutionin quick manner. This algorithm uses only two-pointcrossover genetic operators. A third genetic operator maybe introduced to further improve the performance of theevolutionary algorithm. The evolutionary algorithm maybe modified to handle complex supply chain problem.Stefan Minner [80] did a comprehensive review ofmultiple-supplier inventory models in supply chainmanagement. SCM discusses strategic aspects of suppliercompetition, operation flexibility, global sourcing andinventory models. Further it was extended to logisticsand multi echelon system. The emerging importance of E-business, especially E-procurement possibilities with theuse of Internet technologies reduces transaction costs forsupplier search and order placement with several suppliersand therefore multiple-supplier models are more attractivewhen compared to single sourcing alternative. This type ofmarket with spot offers, continuously changing suppliersand high uncertainty with respect to lead-time andreliability of supplies, makes multiple-supplier replenish-ment and inventory strategies outperform single sourcingpolicies. Matheo et al. [47] have carried out a case study oninventory management in a multi-echelon spare partssupply chain. This paper clearly shows the close relation-ship between supply chain structure and demand patterns.The problems of managing supply chain with variousnumbers of echelons, multi model, extremely variabledemand and lack of visibility over the distribution channelare discussed. They provided an algorithmic solutionthrough the comprehension of the sources of demandvariability and through a probabilistic forecast and inven-tory management. Isreal David et al. [29] have enumeratedthe vendor-buyer inventory production models. They arguethat there should be a certain degree of independencebetween successive links of the supply chain, to allowflexibility in production management in individual links.They identified the degree of independence and level offlexibility in terms of lot sizing and delivery scheduling in asingle-vendor-single-buyer system. In these lines, appro-priate two-sided vendor-buyer inventory production mod-els are formulated and analyzed.

In all the papers, simulation as well as meta-heuristicscan be used as powerful tools to derive results under

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probabilistic conditions. Future research can be focused inthese lines.

6.6 Special cases

Sarah M. Rayan et al. [69] have defined POLCA system.POLCA stands for Paired Cell Overlapping Loop of Cardswith Authorization. This system assumes that the factoryhas been partitioned into non-overlapping manufacturingcells. POLCA maintains constant WIP (like CONWIP)between every pair of cells that experiences inter cell partmovement. Part release to a cell requires an appropriatekanban cards as well as an authorization from the factoryloading system. CONWIP and POLCA achieve a bettertrade-off between total WIP and total throughput time thanthat of other systems. Their application of single chainanalysis for multiple chain operation raises an openquestion whether a single WIP level should be maintainedfor all products or individual levels for each product.Further, most of the studies use simulation. Hence, futureresearch should be directed to develop improved searchprocedures for finding WIP levels in kanban systems.

Krieg et al. [38] considered a kanban controlledproduction system with 3 or more different productsprocessed on a single manufacturing facility as a decom-posed system. The customers for a product arrive as perpoisson distribution. The service time and set-up changesare product specific and follow exponential distribution. Ifthe customer’s demand cannot be met from stock, thecustomer leaves and satisfies his demand elsewhere (lostsales). The production run continues until the targetinventory level given by the kanbans for the product hasbeen reached. Then the manufacturing facility is set-up forproducing the next product. The basic principle of thedecomposed method is to decompose the original multi-product system into a set of single-product subsystems (onefor each product). Each subsystem is modeled approxi-mately by a continuous-time Markov chain. In this bufferallocation problem, the objective is to minimize theaverage work-in-process subject to a minimum requiredthroughput and a constraint on the total buffer space. Theresults of the decomposition method are compared withthat of exact method and simulation. The key performancemeasures are with small relative errors for the decompo-sition method. As an extension to this work, a decompo-sition algorithm can be developed for multi-productkanban systems with state dependent setups. Takashashiet al. [84] have proposed a decentralized reactive kanbansystem for multi-stage production and transportationsystem with unstable changes in product demand. In theproposed system, the time series data of the demand fromthe succeeding stage are monitored at each stageindividually and unstable changes in the demand aredetected by utilizing control charts. In order to develop acontrol rule of the buffer size, the multi-stage productionand transportation system is decomposed into single-stageprocessing systems and the performance of the decom-posed system is investigated by simulation experiments

under various stable-demand conditions. The performanceof this system shows superior result.

Philipoom et al. [60] have done a kanban design withflexible Toyota’s system. This system can dynamicallyadjust the number of kanbans at each workstation in anunstable production environment according to need/demand and lead time to reduce the cost. The work ofTardif et al. [85] introduces a new adaptive kanban-typepull control mechanism which determines the timings torelease or reorder raw parts based on customer demandsand inventory back orders. In the adaptive pull system, thenumber of kanbans in the system is dynamically readjustedbased on current inventory level and backorder level.Unlike a conventional system, this system absorbs extrakanbans according to the variability in demand. It wasfound from the results of a simulation study of a single-stage, single product kanban system that these systems arebeneficial in production line under variable demandconditions. It shows that this adaptive system under suchconditions outperforms the traditional kanban pull controlmechanism. This adaptive approach may be extended formulti-stage, multi-product kanban system.

Kutc So [40] presents the buffer allocation problem withthe objective of minimizing the average work-in-processsubject to minimum required throughput rate and constrainton the total buffer space availability. Both the balanced andunbalanced lines were considered in this work. On the basisof empirical results, he developed a good heuristic forselecting the optimal buffer allocations. The mathematicalmodel discussed in this paper is based on the twoassumptions that there are always materials available forprocessing at the beginning of the line and that the laststation can never be blocked. But one can assume that thefirst station can start processing when there are jobswaiting which arrived as per poisson arrival pattern(make-to-order environment). In contrast, one can considerthe situation where there is finite buffer after the laststation where the finished items are consumed by demandswith poisson pattern (make-to-stock environment). Un-fortunately, in either case, the resulting Markov chain hasinfinite number of states. So, one can develop simulationmodel as the last resort for studying the problem underthese two environments.

Lai et al. [41] have proposed a system dynamic (SD)methodology for studying the new generation of JIT inelectronic commerce environment. It is a framework forthinking about how the operating policies of a companyand its customers, competitors and suppliers interact toshape the company’s performance over time. The systemdynamic is a study which deals with the information feed-back and its evolution into future decision making. Itprovides a new analysis of logistics policies of thecompany. Future work can be on extending the variablesand elements and to conduct experiments to investigate thestability of the system under various conditions such as thesudden increase in demand and random demand, experi-mentation on the system behaviour of different types ofcustomer and modes of manufacturing.

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The papers by Yannick Frein et al. [97] and Yves Dalleryet al. [100] introduce a new mechanism for the coordinationof multistage manufacturing system called extended kanbancontrol system (EKCS). It depends on two parameters perstage viz., the number of kanbans and base stocks of finishedproducts. This EKCS is evolved from combining classicalkanban and base stock control system. The advantages of theextended kanban control system were compared with thegeneralized kanban control system (GKCS). It was foundthat the capacity of EKCS depends only on the number ofkanbans but not on the base stock of finished parts.

A work done by Chan [6] describes the practicalapproach to determine the optimal kanban size usingsimulation. The research was done basically for singleproduct and multi product manufacturing environments intwo types of JIT production systems, the pull-type andhybrid-type. Their measures of performance obtainedthrough simulation models are compared. For a singleproduct, when there is an increase in kanban size, the fillrate decreases whereas with both in-process-inventory andthe manufacturing lead time increase. For multi-productsmanufacture, when there is an increase in kanban size,there is an increase in the fill rate with a decrease in themanufacturing lead-time. Leyuan Shi and Shulimen [43]have presented a hybrid algorithm for buffer allocationproblem called the hybrid nested partition method (NP) andtabu search method (TS). The nested partitioned method isglobally convergent and can utilize many of the existingheuristic methods to speed up its convergence. In thispaper, tabu-search is incorporated in the nested partitionedframework and it was found that such incorporation resultsin superior solutions. The new algorithm is efficient forbuffer allocation problems in larger production lines. Thenested partitioned method can be enhanced by incorporat-ing any one or a combination of the many other heuristicsviz., elaborate partitioning, sampling, backtrackingscheme, simulation, etc. Then, they can be applied tocombinatorial problems of this type.

7 JIT integration, implementation and benefits

Just-in-time is a manufacturing philosophy by which anorganization seeks continuous improvements. For ensuringcontinuous improvements, it is necessary for any organi-zation to implement and integrate the JIT and JIT relatedareas. If it is practiced in its true sense, the manufacturingperformance and the financial performance of the systemwill definitely improve.

Swanson et al. [83] have reiterated that proper planningis essential for implementation of a JIT manufacturingsystem and a commitment from top management is a pre-requisite. Cost benefit analysis is to be studied initially withthe knowledge of key items such as the cost of conversionto a JIT system and time period of conversion. Cook et al.[11], in their case study for applying JIT in the continuousprocess industry, show improvements in demand forecastand decrease in lead-time variability.

The relationship between implementation of TQM, TPMand JIT will lead to improvement in the manufacturingperformance (Kribty et al. [37]). Further Huang [23]discusses the importance of considering the integration ofTPM, JIT, Quality control and FA (Factory Automization).Imai [27] believes that TQM and TPM are the two pillarssupporting the JIT production system. Kakuro Amasaka[32] proposes a new JIT management system, which helpsto transfer the management technology into managementstrategy.

Fullerton et al. [65] have conducted a study in 253 firmsin USA to evaluate empirically whether the degree withwhich a firm implements the JIT practices affects the firmsfinancial performance. From their study, JIT manufacturingsystem will reap sustainable rewards as measured byimproved financial performance. Also, they studied thebenefits of JIT implementation in 95 firms in USA. Theyhave concluded that JIT implementation improves theperformance of the system, because of resultant qualitybenefits, time based benefits, employees flexibility,accounting simplification, firms profitability and reducedinventory level.

8 Conclusion

The growing global competition forces many companies toreduce the costs of their inputs so that the companies canhave greater profit margin. There are considerableadvancements in technology and solution procedures inreality, to achieve the goal of minimizing the costs ofinputs. JIT-KANBAN is an important system, which isused in production lines of many industries to minimizework-in-process and throughput time, and maximize lineefficiency. In this paper, the authors have made an attemptto review the state-of-art of the research articles in the area“JIT-KANBAN system”. After a brief introduction to pushand pull systems, different types of kanban and theiroperating principles, blocking mechanisms, the authorshave classified the research articles under JIT-KANBANsystem into five major headings, viz., empirical theory,modeling approach, variability and its effect, CONWIP andJIT-SCM. Also, the authors have provided a section forspecial cases under JIT-KANBAN. This paper would helpthe researchers to update themselves about the currentdirections and different issues under JIT-KANBANsystem, which would further guide them for their futureresearches.

The directions for future researches are presented below.The flow shop as well as mixed model assembly line

problems come under combinatorial category. Hence,meta-heuristics viz., simulated annealing, genetic algo-rithm and tabu search may be used to find solution todetermine the minimum number of kanbans and othermeasures. In simulated annealing algorithm, researcherscan aim to device a better seed generation algorithm whichwill ensures better starting solution. In most of the papers,comparisons are done only based on relative improve-ments. Instead of this approach, comparisons based on

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complete ANOVA experiments would provide reliableinferences.

Under batch processing system with multiple producttypes, research may be directed to study the effect ofdifferent combinations of probability distributions forarrival process and processing times on the averageinventory level of each product as finished goods.

As an extension to the work of Markham et al. [28] on aprocedure based rule induction approach for determiningthe number of kanbans and other factors in JIT, develop-ment of knowledge acquisition for this domain will be asignificant contribution to literature.

Sarkar et al. [72] did stage-wise optimization for a multistage kanban system for short life-cycle product in themarket. This work may be extended for multi-product withvarying production rate at each workstation in anassembly-type production.

As an extension of the work of Mehmet Savsar et al.[48], a study can be carried out to compare the effects ofdifferent combinations of the two kanban withdrawalpolicies and number of kanbans between workstations, onthe performance measures. In the work of Yavuz and Satir[98] on multi-item, multi-stage flow line, simulation modelmay be developed with feeder lines introduced into the pullsystem configuration, where lines feed the final assemblyline. Further, alternate operating routes for the productsalong the line may be introduced.

As an extension to the work of Chaturvedi and Golhar[7], a CONWIP system with generalized, n stations inseries may be analyzed.

This algorithm developed by Elizabeth Vergara et al.[18] uses only two-point crossover genetic operators. Athird genetic operator may be introduced to further improvethe performance of the evolutionary algorithm. Theevolutionary algorithm may be modified to handle com-plex supply chain problem. In JIT-SCM related researchworks, effort should be directed to develop simulation aswell as meta-heuristics to derive results under probabilisticconditions.

In the work of Sarah M. Rayan et al. [69], the applicationof single chain analysis for multiple chain operation raisesan open question whether a single WIP level should bemaintained for all products or individual levels for eachproduct. Further, most of the studies use simulation. Hence,future research shall be directed to develop improvedsearch procedures for finding WIP levels in kanbansystems. As an extension to the work of Krieg et al. [38],a decomposition algorithm can be developed for multi-product kanban systems with state dependent setups. Theadaptive approach suggested by Tardif et al. [85] may beextended for multi-stage, multi-product kanban system.The work of Lai et al. [41] can be extended by includingmore variables and elements and conducting experimentsto investigate the stability of the system under variousconditions such as the sudden increase in demand andrandom demand, experimenting on the system behaviour ofdifferent types of customer and modes of manufacturing.The nested partitioned method provided by Leyuan Shi andShuli Men [43] can be enhanced by incorporating any one

or a combination of the many other heuristics viz.,elaborate partitioning, sampling, backtracking scheme,simulation, etc. Then, they can be applied to combinatorialproblems of this type.

Ants colony optimization algorithm is a recent inclusionto the existing meta-heuristics viz., simulated annealingalgorithm, genetic algorithm and tabu search. So, aresearcher can study the solution accuracy as well asrequired computational time of this algorithm for his/herJIT problem of interest, which falls under combinatorialcategory and compare its results with the results of theother three heuristics (meta-heuristics).

Acknowledgement The authors thank the unanimous referees fortheir constructive criticisms, which helped them to improve thecontent and presentation of this review paper.

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