This document provides a literature review of Just-in-Time (JIT) and Kanban systems. It surveys 100 research papers on the topic and discusses key aspects of JIT-Kanban systems including the philosophy of JIT, push and pull production systems, blocking mechanisms in Kanban systems, measures of performance, and recent trends/future directions for research. The review aims to critically analyze and classify JIT literature, explore recent trends in JIT-Kanban, and identify directions for future research.

1. Int J Adv Manuf Technol (2007) 32: 393–408
DOI 10.1007/s00170-005-0340-2
ORIGINA L ARTI CLE
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)-KANBAN zero inventories, zero breakdown and so on. It ensures the
literature survey was carried out and presented. The supply of right parts in right quantity in the right place and
introductory section deals with the philosophy of JIT, and at the right time. Hence, the old system of material
the concept involved in the push and pull system. The acquisition and, buyer and seller relationships are changed
blocking mechanisms in the kanban system are also to new revolutionary concepts (Womack et al. [91],
discussed elaborately. Besides these sections, the impor- Womack and Jones [92], Markey et al. [45]). Similarly,
tance of measure of performance (MOP) and the applica- JIT becomes an inevitable system at plant level, which
tion of the same with respect to JIT-KANBAN are integrates the cellular manufacturing, flexible manufactur-
presented. The recent trends in the JIT-KANBAN are ing, computer integrated manufacturing and Robotics
discussed under the heading “Special cases”. In this review, (Schonberger [63], Golhar [12]).
100 state-of-art research papers have been surveyed. The Due to the technological advancement, the conventional
directions for the future works are also presented. method of push production system linked with Material
Requirement Planning (MRP) was changed to pull type JIT
Keywords JIT . KANBAN . Blocking Mechanisms . production system to meet out the global competition,
CONWIP . Measure of performances (MOP) . Simulation where the work-in-process (WIP) can be managed and
controlled more accurately than the push- production
system (Mason Paul [46]).
1 Introduction KANBAN system is a new philosophy, which plays a
significant role in the JIT production system. Kanban is
Just -In-Time (JIT) manufacturing system was developed basically a plastic card containing all the information
by Taiichi Ohno which is called Japanese “Toyota required for production/assembly of a product at each stage
production system”. JIT manufacturing system has the and details of its path of completion. The kanban system is
primary goal of continuously reducing and ultimately a multistage production scheduling and inventory control
eliminating all forms of wastes (Brown et al. [5], Ohno system. These cards are used to control production flow
[54], Sugimori et al. [82]). Based on this principle, and inventory. This system facilitates high production
Japanese companies are operating with very low level of volume and high capacity utilization with reduced produc-
inventory and realizing exceptionally high level of quality tion time and work-in-process.
and productivity (Richard J. Tersine [62], James H. Greene The objectives of this paper are as listed below
[30]). JIT emphasizes “zero concept” which means
1) Critical review of JIT literature.
achievement of the goals of zero defects, zero queues,
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 appropriate
C. Sendil Kumar
Neyveli Lignite Corporation, classification is presented.The kanban study was made
Neyveli, India elaborately, since it acts as a basic communicator and feed-
back agent to the JIT system. Push and pull system,
R. Panneerselvam (*) principle of operation of kanban cards, Blocking mecha-
Department of Management Studies,
School of Management, Pondicherry University, nism, Toyota’s formula, and the measures of performances
Pondicherry 605 014, India (MOP) are also discussed in this paper. The latest trends in
e-mail: panneer_dms@yahoo.co.in JIT-Kanban system are also addressed separately under the

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heading “Special cases”. Finally, the directions for future Request for items Request for items
researches are presented.
WS 1 WS 2 STORE
2 Push and pull systems Items movement Items movement
Fig. 2 Pull system
Push and Pull system are two types of production systems,
which operate equally in opposite sense and have their own
merits and demerits (Monden [50], Villeda Ramiro et al. (WK), respectively. A schematic diagram of a two-card
[89]). system is shown in Fig. 4.
Push system It is a conventional system of production.
When a job completes its process in a workstation, then it 2.1 Operation of two-card kanban system
is pushed to the next workstation where it requires further
processing or storing. In this system, the job has a job card The two-card kanban pull system which works in
and the job card is transferred stage by stage according to the Assembly/Manufacturing line is elaborated by
its sequence. In this method, due to unpredictable changes Panneerselvam [56], Edward J. Hay [17], Kimura and
in demand or production hinder-ness, the job happens to Terada [36], Hunglin Wang et al. [25] and Hemamalini et
deviate from its schedule and it causes accumulation of al. [21] and Shahabudeen et al. [76]. Basically it has plastic
work-in-process inventory. Hence, inventory planners cards, which give information about the parts and also
pessimistically fix the safety stock level on the higher things to be done. The production order kanban (POK) is a
side. A schematic representation of the push system is production order, which instructs the preceding work-
shown in Fig. 1. In Fig. 1, WSj is the jth workstation and station to produce the required number of units. The
the product line consists of n workstations. withdrawal kanban (WK) gives the message to the
succeeding process about the number of units it should
Pull system A pull type production system consists of a withdraw.
sequence of workstations involving value addition in The simple steps adopted in kanban system are as
each workstation (WS). In the pull system, from the follows
current workstation (j), each job is withdrawn by its
1) The container of the succeeding workstation j+1 is
succeeding workstation (j+1). In other words, the job is
moved to the preceding workstation j with the
pulled by the successive workstation instead of being
withdrawal kanban (WK) and placed it in its output
pushed by its preceding workstation. The flow of parts
buffer.
throughout the product line is controlled by Kanban Cards
2)
(Turbo [87]). In practice, these kanban cards can be either
a) Consequently it pulls the parts from output buffer of
“single-card system” or “two-card system”. Each work-
the workstation j and detach the production order
station has an inbound stocking point and an outbound
kanban (POK) attached to those parts and then places
stocking point. The primary advantage of the pull system
the POK in the POK-post of the workstation j.
is the reduced inventory and hence the associated cost of
b) Work station j starts its production as per the
inventory reduction. A schematic view of the pull system
production order in its POK post.
with two workstations and store is shown in Fig. 2.
A kanban system operates only with single card is
called production order kanban (POK) (J. Berkley [4], 3) The container along with the parts and WK moves
Sarathapreeyadarishini et al. [78]). If the distance between again to its succeeding workstation j+1. Then it
the consecutive workstations is very short, a single buffer delivers the parts to the input buffer of the workstation
mode is made available between the workstations. This j+1 and places the WK to the WK-post of the
buffer mode acts as both outbound buffer for the current workstation j+1.
workstation j and inbound buffer for the succeeding
workstation j+1, respectively. A schematic diagram of a
single-card system is shown in Fig. 3. In the two-card POK Only card movement
system, where the distance between the two consecutive POST
work stations are more, each work station will have
separate inbound buffer and outbound buffer (Kimura O. et
al. [36], Hemamalini et al. [21]) and the cards are called as
Production Order Kanban (POK) and Withdrawal Kanban Card + Parts movement
WSj WS j+1
WS1 WS2 ooo WSj ooo WSn STORE
BUFEER
Fig. 1 Push system Fig. 3 Schematic diagram of a single card system

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POK WK 3.1.1 Blocking due to part type
POST POST
(j) (j+1)
This type of blocking occurs due to restriction in the
number of parts (containers) that can be stored in the buffer
POK WK
between workstation j and the workstation j+1. The
workstation j will not process the particular part p, since
there is no reserved space in the buffer storage for the
WS WK+ Parts WS particular part type.
j j+1 Let Q(p, j, j+1) be the maximum number of units
Output Buffer Input Buffer (container) of part type p that can be stored in the buffer
of of storage between the workstation j and the workstation j+1.
Workstation j Workstation j+1 Then the workstation j can process the p type parts, only if the
actual number of units (container) of the part type p in the
WK buffer storage less than Q(p, j, j+1); otherwise, the work-
station j is blocked due to part type p alone. The workstation
Fig. 4 Schematic diagram of two card system can process any other part type provided that workstation is
not blocked with respect to that part type.
4) When the parts in the containers of the workstation j+1
are fully used, then the steps from 1 to 3 are repeated.
3.1.2 Blocking due to queue size
3 Blocking mechanisms This type of blocking occurs due to restriction in the total
number of containers of all part types in the buffer between
Each workstation of a production/assembly line requires workstation j and the workstation j+1. The workstation j
sufficient space for storing parts in its output buffers. When will not process any part type if there is no space in the
the buffer capacity of a workstation is fully occupied, no buffer storage between the workstation j and the work-
further storage is possible. Because of this fact, the station j+1, irrespective of part type and container.
workstation can not release the parts and hence, it can Let Q (j, j+1) be the maximum number of containers
not process components. This condition is called “Block- irrespective of the part types that can be stored in the buffer
ing”. The blockings are categorized according to the types storage between the workstation j and the workstation j+1.
as presented in Table 1. Then the workstation j can process part types, only if the
actual total number of containers in the storage between the
workstation j and the workstation j+1 is less than Q(j, j+1);
3.1 Single card -instantaneous Otherwise, the work station j is said to be blocked due to
the queue size constraint.
As discussed earlier, if the workstations are situated closer
to each other, the output buffer of the workstation j and the
input buffer of the workstation j+1 are one and the same. 3.1.3 Dual blocking mechanism
Under such situation, a single card instantaneous kanban is
used. Berkley [4] and Sharadhapreeyadarishini et al. [77] If both the above blocking mechanisms operate simulta-
have discussed the blocking mechanism of single card type neously, then it is called Dual blocking mechanism.
in detail. The work station j is said to be blocked if the actual
number of units (containers) of the part p in the buffer
storage between the workstation j and the workstation j+1
is equal to Q(p, j, j+1) and the actual total number of
containers in the buffer storage between the workstation j
and the workstation j+1 is equal to Q(j, j+1).
Table 1 Categories of blocking mechanisms Subsequently, when a container of the part type p is
taken by workstation j+1, then the blocking is released and
Single Card -Instantaneous Two Card - Non Instantaneous
the workstation j can start processing the part p. If the work
1) Blocking due to part-type. 1) Blocking due to part-type. station j+1 takes the container of any part other than that of
2) Blocking due to queue size. 2) Blocking due to queue size. p, then the work station j is still blocked with respect to part
3) Dual blocking mechanism. 3) Dual blocking mechanism. p and it is not blocked with respect to other part types.
Blocking mechanism Operative
on Material Handling.
4) Blocking due to part-type.
3.2 Two card- non-instantaneous
5) Blocking due to queue size.
6) Dual blocking mechanism.
If the distance between consecutive workstations is more,
there will be independent input and output buffer points for

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each workstation. In this system, the blocking can occur 3.3 Blocking mechanisms operative on material
due to stagnation of parts in the output buffer of that handling
workstation. Berkley J. [4] et al. [21] have studied this type
of blocking. Material handling operation between the workstation j and
the workstation j+1 can be blocked due to part type, queue
size or both. This is similar to the above types but the
3.2.1 Blocking due to part type blocking is due to Material Handling (MH) between output
buffer of the workstation j and the input buffer of the
This type of blocking occurs due to restriction in the workstation j+1. This was studied by Berkley J. [4] and
number of parts (containers) that can be stored in the output Hemamalini et al. [21].
buffer of the workstation j. The workstation j can not
process the particular part p, since there is no reserved
space for the part type p in the output buffer of the 3.3.1 Blocking mechanism due to part type
workstation j.
Let Q(p, j) be the maximum number of units (containers) This type of blocking occurs due to restriction in the
of the part type p that can be stored in the output buffer number of parts (containers) that can be stored in the input
storage of workstation j. Then the workstation j can process buffer of the workstation j+1.
the parts of the part type p, only if the actual number of Let M(p, j+1) denotes the maximum number of units
units (containers) of p in the output buffer storage of the (containers) of part type p that can be stored in the input
workstation j is less than Q(p, j); otherwise, the workstation buffer storage of the workstation j+1.
j is blocked due to the part type p alone. The workstation j Then, materials handling is permitted from the output
can process parts of any other part type provided that the buffer of work station j to the input buffer of workstation
workstation is not blocked with respect to that part type. j+1, if the actual number of units (containers) of part p in
the input buffer of work station j+1 is less than M(p, j+1).
3.2.2 Blocking due to queue size
3.3.2 Blocking mechanism due to queue size
This type of blocking occurs due to restriction in the total
number of containers of all part types in the output buffer of This type of blocking occurs due to restriction in the total
the workstation j. The workstation j will not process any of number of containers of all part types that can be stored in
the part types since there is no space in the output buffer the input buffer of the workstation j+1.
storage of the workstation j, irrespective of part type and Let M(j+1) be the maximum number of containers
container. irrespective of part types that can be stored in the input
Let Q(j) denotes the maximum number of containers buffer storage of workstation j+1. Then, the material
irrespective of part type that can be stored in the output handling is permitted from output buffer of the workstation
buffer storage of the workstation j. j to the input buffer of workstation j+1 only, if the actual
Then the workstation j can process parts only if the total number of containers in the input buffer of work
actual total number of containers in the output buffer of the station j+1 is less than M(j+1).
workstation j is less than Q(j); otherwise, the workstation j
is said to be blocked due to the queue size constraint.
3.3.3 Dual blocking mechanism
3.2.3 Dual blocking mechanism If both blocking mechanisms discussed in sections 3.3.1
and 3.3.2 operate simultaneously then it is called dual
If both of the above blocking mechanisms operate simul- blocking mechanism. The material handling operation is
taneously, then it is called dual blocking mechanism. said to be blocked, if the actual number of units (contain-
The workstation j is said to be blocked if the actual ers) of part type p in the input buffer of the workstation j+1
number of units (containers) of part type p in the output is equal to M(p, j+1) and the actual total number of
buffer of workstation j is equal to Q(p, j) and the actual total containers in the input buffer of the workstation j+1 is
number of containers in the output buffer of workstation j is equal to M(j+1). Subsequently, when a container of the part
equal to Q(j). type p is taken from the input buffer of workstation j+1, the
Subsequently, when a container of part type p is taken to blocking will be released and the material handling starts to
the input buffer of the workstation j+1, the blocking will be clear the parts from the output buffer of the work station j.
released and the workstation j can start processing the part Now, the workstation j can start processing the part p. If the
type p. If the input buffer of workstation j+1 takes a workstation j+1 takes a container of parts other than that of
container of parts other than the part p, then the workstation part p from the input buffer of the workstation j+1, then the
j is still blocked with respect to the part type p. material handling is not possible for the part p. So the
workstation j is continued to be in blocked state with

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respect to the part p. However, the workstation j is not Table 2 Factors used by various researchers
blocked with respect to other part types. Sl. Factors used The reference numbers
No for MOP of research articles which
use the MOP
4 Toyota’s kanban formula
1 Average WIP [2, 6, 15, 48, 76, 79, 89, 90, 98]
The formula used by Toyota Motor Company to determine 2 Demand [6, 15, 76]
the number of kanbans is called Toyota formula. (Berkley 3 Fill Rate [6]
[4], Chan [6], Henry et al. [10], Hunglin Wang et al. [25], 4 Average kanban [74, 77, 98]
Ohno et al. [53], Monden Y. [50], Philipoom et al. [60] and waiting/queue time
Yavuz et al. [98]). The Toyota’s kanban formula is 5 Average Flow [2, 6, 22, 31, 51, 61, 76, 77, 98]
presented below. (production lead) time
6 Average setup/process [98]
D Lð1 þ αÞ
K time ratio
C 7 Average input/output [79, 89]
inventory
where, 8 Mean cumulative [48, 74, 88, 90, 98]
K is the number of kanbans, throughput rate
D is the demand per unit time, 9 Mean line utilization [48, 89, 98]
L is the lead-time, 10 Mean demand [98]
α is the safety factor and satisfaction lead time
C is the container capacity 11 Mean staging delay of job [49, 51]
From these literatures, it was noted that the lead-time 12 Mean Tardiness [2, 22, 51, 61, 79]
includes waiting time, processing time, conveyance time 13 Weighted earliness of the [22, 61]
and kanban collecting time. The safety stock serves as a job
buffer against variations in both supply and demand. Henry
et al. [10] has suggested some practical values for the
variables C and α. The value of C is limited to a maximum frequently used as performance measures. Some important
of 10% of demand and α is a policy variable, which is definitions for the factors, which are used in different
decided by the management up to 10% of the demand. The MOPs by various researchers, are discussed below.
variable K is the number of kanbans, which is related to the Yavuz and Satir [98] have used seven factors in their
stock. If the value of K increases, the stock of the parts also study, which are as presented below.
increases. As a result, idle stock occurs. Similarly, if the
1) Mean Cumulative Throughput Rate: It is the ratio of
value of K decreases, the stock of parts also decreases and
total satisfied demand to the total generated demand.
shortage occurs. Hence, the JIT production system applies
2) Mean Total Production Lead Time: It is the amount of
trade-off between the above parameters to find the optimum
time spent by a job from entering the system to until
number of kanbans. Many researches have been carried out
completion of all operations, averaged over all
to find the optimum number of kanbans using different
completed job.
methodologies and tools such as simulation, queuing
3) Mean Total Demand Satisfaction Lead Time: It is the
models, mathematical models, Artificial Intelligent ap-
time interval between arrival of the demand and
proach and so on. From the Toyota’s empirical equation,
satisfaction of the demand.
one can find the number of kanbans required for the system.
4) Mean Utilization of Line: It is the mean utilization of
the last station in the line.
5) Mean Setup/Run Time Ratio of Line: It is the ratio
5 Measure of performance (MOP)
between the setup time and the run time of last station.
6) Mean Total WIP Length: It is the mean of all in-
For any system, the efficiency is measured through a
process-inventory levels for the products excluding
function of related parameters/ factors. Hence these factors
finished goods (FG).
must obviously establish close relationship with the
7) Mean Total Waiting Time: It is the waiting time of all
focused problem. These factors individually or jointly repre-
products in all processes and finished goods inventory
sent a performance. Blair Berkly J. [4] has given a note on
(FGI).
workstation performance in kanban controlled shops in
terms of average inventories, quality and the ability to meet A general purpose analytical model to evaluate the
the demands. Our study reveals that various researchers performance of multistage kanban controlled production
have used thirteen factors and they are shown in Table 2. system was developed by Di Mascolo et al. [15]. The
From Table 2, it is inferred that the average work-in- performance measures used by them are percentage of
process (WIP), average flow time, mean cumulative demand for back-order, average waiting time of back-
throughput rate and weighted earliness of the job are order and average work-in-process.

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A simulation experiment to evaluate the relative effective- Table 3 Results of the study by Chan [6]
ness of various rescheduling policies in capacity-constrained, MOP Pull Hybrid Hybrid
JIT make-to-stock production environment is examined by (Single (Single (Multi-product)
Kern et al. [34]. Three performance measures analyzed by product) product)
them are average finished goods inventory, total units of sales
lost, and measure of schedule instability. Jing-Wen Li [31] 1) Fill rate Decrease Decrease Increase
has measured three factors for shop performance which are 2) In-process- Increase Increase Increase
average work-in-process (WIP) inventory, average flow time inventory
and average set up time to processing time ratio (ASOTR), 3) Manufacturing Increase Increase Decrease
which is the ratio of total amount of time spent for setting up lead time
machines to the total amount of time spent for processing
parts averaged over all machines. Uday S. Karmarker [88]
used throughput rate for total work performance. In another
study, the priority rule assignment was checked by the 6 Literature review
following factors by Nabil R. Adam et al. [51].
Golhar et al. [12] have classified the JIT literature as
1) The lead time of a job
elimination of waste, employee participation, supplier
2) The flow time of the job
participation and total quality control.
3) The staging delay of a job
A similar work was done by Berkly [4] for kanban
4) Mean Tardiness
production process. He has selected 24 elements in the
Hemamalini et al. [22] considered the objective function kanban production system as operational design factors.
to minimize the sum of weighted flow time, weighted In this section, the different topics associated with “JIT-
earliness of jobs and weighted tardiness of containers. KANBAN” studied by various researchers have been
Shahabudeen et al. [76] used an universal test which may grouped and presented as shown in Fig. 5. The Table 4
be suited for the MOP in any JIT system, which are shows the reference numbers of the articles with respect to
percentage zero demand (PZD), mean lead time (MLT) and the classifications shown in Fig. 5.
mean total WIP (MTW) as explained below. Obviously, most of the researchers were focusing on the
determination of number of kanbans and determining
1) Percentage zero demand: It is the percentage of total
corresponding solutions by using suitable models and
demand immediately satisfied to the total generated
tools. Some authors have developed simulations model and
demand.
meta-heuristics like, genetic algorithm (GA), tabu search
2) Mean lead time: It is the sum of the waiting time,
(TS), and simulated annealing (SA) for JIT-Kanban for
processing time and moving time averaged per station.
better solutions.
It is also called as mean flow time.
The Table 5 shows the number of articles dealt in
3) Mean total WIP: It is the average number of kanbans
different periods. From, Table 5, it is clear that, during last
waiting for each part type at each workstation.
two 5 years period (1996-2000 2001-2005), the number
Here, PZD is a maximization measure and, MLT and of researches are more. Further, more researches have been
MTW are the minimization measures and hence the sum of done in empirical theory, flow shop, simulation, variability
the objective MOPs is changed as Zmax (a1PZD +a2 RMLT and its effects, CONWIP and special cases. Many
+a3 RMTW), where a1, a2 and a3 are weights of the researchers have worked in JIT system with different
respective measures and, RMLT and RMTW are modified objectives. Here, the authors have grouped some important
values of MLT and MTW, respectively.
Chan F.T.S. [6] has done a work on how the MOP JIT
changes in different production systems, while increasing
the kanban size. The measures of performance taken by SPECIAL CASES
him are as listed below
1) Unsatisfied order, which is the difference between the KANBAN CONWIP
actual number of unit produced and the level of SCM
demand.
2) Manufacturing lead-time, which is the time between POLCA
the customer order and the completion of order. VARIABILITY
3) In-process-inventory is the total number of work-in- ITS EFFECTS
process (WIP) inventory in units excluding finished FLOW SHOP
DIFFERENT MODELS
goods inventory. ASSEMBLY MATHEMATICAL
4) Fill rate is the percentage of demand satisfied. LINE QUEUING
MARKOVIANS
SIMULATIONS
The results of his study as a function of the kanban size BATCH COST MINIMIZATION
are shown in Table 3.
Fig. 5 Flowchart showing the classification of literature review

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Table 4 Details of classification of review articles Table 6 Objective based classification and their references
Area of Research Reference numbers of related Articles Classification Reference numbers of articles
JIT [4, 12] A. Principles of [21, 50, 55, 59, 96]
Kanban-Empirical theory [10, 33, 50, 59, 71, 93, 95] JIT-Kanban system
Flow shop [7, 22, 58, 61, 77, 78] B. Operating Factors [4, 12, 80]
Assembly line [16, 89, 94] C. Design of Kanban [1, 10, 13, 19, 26, 52, 53, 66, 71, 74, 75,
Batch Production [35, 86] System 93]
System D. Performance [7, 24, 33, 36, 48, 58, 73, 81, 89, 90, 98,
Modeling Approach: [3, 36] behaviour 99]
Mathematical E. Sequencing [18, 22, 57, 58, 61, 77, 78, 94]
Queuing [73, 99] Scheduling
Marko-chain [14, 28, 52, 90] F. Inventory/Buffer [3, 68, 86]
Simulation [1, 9, 13, 19, 26, 66, 67, 74, 75] Control
Cost minimization [53, 68, 72]
Variability and its effects [7, 24, 48, 89, 98]
CONWIP [8, 44, 55, 69, 70, 79, 96]
POLCA [69] Karmarker and Kekre [33] have concluded from their
SCM [18, 29, 47, 80]
studies that the reduction in container size and increase in
Special Cases [6, 38, 40, 41, 43, 60, 69, 84, 85, 97, 100]
number of kanbans lead to better results. Many researchers
were interested in finding the optimal number of kanbans.
The Toyota formula is very much useful in determining the
optimal number of kanbans.
objectives of the researches into six headings as shown in Co Henry et al. [10] used the Toyota formula and also
Table 6. From Table 6, it is clear that the following investigated the safety stock allocations in an uncertain
objectives attracted more researchers. dynamic environment. A similar work was considered by
Sarkar et al. [71] to find number of kanbans between two
– Design of kanban system
adjacent workstations. Yale T. Herer et al. [95] presented a
– Performance behaviour
study for kanban system, CONWIP and buffered produc-
– Sequencing and scheduling
tion lines. In this study, they incorporated a non-integral
approach using simulation. The use of non-integral
approach helps production planners to obtain discrete
6.1 Empirical theory number of kanbans.
Woolsey et al. [93] have developed a simple spreadsheet
In the paper by Monden Y. [50], a comprehensive optimization program to determine the corresponding
presentation of Toyota production system is given. A number of kanbans with respect to user-defined safety
successful kanban system will drastically reduce the stock levels and other values. It gives a close-form of
throughput time and lead time (Philipoom et al. [59]).
Table 5 Details of researches in different periods
Area of Research 1980-1985 1986-1990 1991-1995 1996-2000 2001-2005 Total
JIT 2 2
Kanban- Empirical theory 1 2 3 6
Flow shop 1 4 1 6
Assembly line 1 1 1 3
Batch 1 1 2
Modelling Approach: Mathematical 1 1 2
Queueing 2 2
Markovians 1 2 1 4
Simulation 1 1 5 2 9
Cost minimization 1 1 1 3
Variability and its effects 1 1 3 5
CONWIP 2 3 2 7
POLCA 1 1
SCM 4 4
Special cases 1 1 2 7 11
Total 3 10 11 23 20 67

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solution to the problem. This means that an answer for any and bound algorithm, and simulated annealing algorithm
problem size may be instantaneously obtained. for finding the optimal solution and sub-optimal solution of
the mixed-model sequencing problem, respectively to
minimize the total conveyor stoppage time. The branch-
6.1.1 Flow shop and-bound method was devoted to find the optimal
solution of small-sized problems, whereas the simulated
Kanban system is widely implemented in repetitive annealing method was used to cope with large-scale
manufacturing environment. For a single card operational problems to obtain a good sub-optimal solution. Future,
system, Sharadhapriyadarishini et al. [77] have developed research on simulated annealing applied to this problem
two heuristics and proved that these are more efficient. can be directed to establish a better seed generation
Saradhapriyadarishini et al. [78] have proposed a recursive algorithm. However, the practitioner should spend con-
equation for scheduling the single card kanban system with siderable time in fixing the parameter called temperature
dual blocking. They proposed a heuristic with twin (T) in the simulated annealing algorithm by trail and error
objectives of minimizing the sum of total weighted time method before actually solving the problem.
of containers and weighted flow time of part-types.
Rajendran [61] has done a work on two card flow shop
scheduling with n part-types. In this paper, mathematical 6.1.3 Batch production system
models for time tabling of containers for different problems
have been formulated. Then, a heuristic was developed to In a batch production system, the switching over from one
minimize the sum of weighted flow time, weighted earliness, product to other product depends on many factors such as
and weighted tardiness of containers. Hemamalini et al. [22] stock reaching to the threshold level, different priority
have done similar work. In this work, the heuristic developed schemes, economical setups, etc. Tafur Altiok et al. [86]
is simulated annealing algorithm. This is compared with have dealt this issue differently for the pull type
random search method. In these papers, the comparisons are manufacturing system with multi product types. In this
done only based on mean relative percentage increase. paper, they developed an iterative procedure to approxi-
Instead of this approach, comparisons based on complete mately compute the average inventory level of each
ANOVA experiments would provide reliable inference. product as finished goods using different priority schemes.
Peter Brucker et al. [58] have carried out research on In this paper, the demand arrival process is assumed to be
flow shop problem with a buffer of limited capacity a poisson distribution and processing times and the set-up
between two adjacent machines. After finishing the times are arbitrarily distributed. But, in practice, the
processing of a job on a machine, either the job is to be processing times may follow other distributions, viz.,
processed on the following machine or it is to be stored in normal, uniform, exponential, etc. which are not experi-
the buffer between these machines. If the buffer is mented in this paper. Khan et al. [35] addressed the
completely occupied, the job has to wait on its current problem of manufacturing system that procures raw
machine but blocks this machine for other jobs. In this materials from vendors in lot and convert them into
paper, they determined a feasible schedule to minimize the finished products. They estimated production batch sizes
makespan using tabu search. The results of the problem for JIT delivery system and designed a JIT raw material
using tabu search were compared with that of benchmark supply system. A simple algorithm was developed to
instances. The comparisons are done only based on relative compute the batch sizes for both manufacturing and raw
improvements. Instead of this approach, comparisons material purchasing policies.
based on complete ANOVA experiments would provide
reliable inference.
6.2 Modeling approach
6.1.2 Assembly line Modelling approach aims to obtain the optimal solution.
This subsection reviews different modeling approaches.
Assembly lines are similar to the flow shops in which
assembly of parts are carried out in a line sequence. In a
multi product assembly line, the sequencing of the jobs is a 6.2.1 Mathematical model
challenging task. Drexl et al. [16] considered an assembly
line sequencing mixed model problem. It is a combinatorial Kimera and Terada [36] have developed a mathematical
problem. They formulated this combinational problem as model in the area of kanban system. They have given a
integer programming model. This model can be used only basic balance equation for multi stage systems, which
for small size problems due to the limitations of operations shows how the fluctuation of final demand influences the
research software with respect to handling the number of fluctuation of production and inventory volumes. Bitran
variables and constraints, which are present in the integer- and Chang [3] have designed an optimization model for the
programming model. Xiaobo et al. [94] have considered kanban system. The model is intended for a deterministic
similar work on mixed model assembly line sequencing multi-stage capacitated assembly-type production setting.
problem with conveyor stoppages. They proposed branch In this paper, a non-linear model developed by them is

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converted into a linear model with deterministic demand. approach involves 3 steps methodology, viz., 1) data
This deterministic model is designed to find the choice of collection, 2) formation of decision tree, and 3) interpre-
the number of kanbans to be used at each stage of a given tation of decision tree. This method helps to set kanban
problem and to control the level of inventory. But this levels under high demand variability. The results show that
analysis does not include uncertainties directly. Hence, the rule induction using CART is a viable solution to the
utility of this model is very much limited. knowledge acquisition bottleneck. Hence, an extended
work on knowledge acquisition for this domain will be a
significant contribution to literature.
6.2.2 Queuing model
Seki et al. [73] have designed a single-stage kanban system 6.2.4 Simulation based studies
with poisson demand arrivals. The system is formulated as
a queuing system under piecewise constant load, and a There are many simulation softwares available in the
numerical method by transient solutions of the queue is market, such as GPSS, Q-GERT, SLAM-II, SIMAN,
applied. This method, which shows the transient behavior SIMSCRIPT, EXTEND, ARENA, and SIMULINK. Sim-
of the kanban system, gives a better result. Yoichi Seki et ulation uses the attributes/parameters of a problem to arrive
al. [99] did similar work on the single stage kanban system the results. As for as designing of kanban system, a basic
with poisson demand and erlang production times. The simulation study was done by Davis et al. [13] and Gabriel
objective of this work is to determine the number of et al. [19] to determine the number of kanbans. In another
kanbans, when a change of load to the system is planned. work by Rudi De Smet et al. [67], a simulation model was
They mainly proposed a numerical method by transient developed to study the feasibility of plans to produce some
solutions of the queueing system which was developed subparts of the product in a kanban-controlled manner to
under piecewise constant load. This method also shows determine the operational parameters such as number of
that the transient behavior of the kanban system operates kanbans and container size. This feasibility study was
better with other parameters. In this paper, the load carried out for two situations, namely (1) all subpart types
distribution is assumed to be piecewise linear. Instead, it are produced in a kanban controlled manner and (2) only
can be assumed as a continuous distribution and the the production of fast-movers on two (out of three)
corresponding results using simulation can be compared machines is kanban controlled. The result assures that the
with `the results of this paper. kanban control is the best method for fast moving parts.
In a kanban control system, the main decision
parameters are the number of kanbans and lot size. Alabas
6.2.3 Markovians model et al. [1] developed three-meta heuristics viz., genetic
algorithm (GA), simulated annealing (SA) and tabu search
Vito Albino et al. [90] studied a model of kanban controlled (TS) coupled with a simulation model to find the optimum
manufacturing system based on Markovian assumption. number of kanbans with the minimum cost. In addition, a
An approximate approach was developed to solve the neural network metamodel was developed and compared
model, which permits reliable evaluation of performance in with the heuristic procedures in terms of solution accuracy.
terms of throughput time and work-in-process (WIP). They found that the tabu search requires less computational
Further, they validated the results using discrete-event efforts when compared to the other two meta-heuristics and
simulation applied to their problem. It was observed that the neural network meta-model. In a similar work by
the results of the approximation approach did not deviate Hurrion R.D. [26], simulation and neural network meta-
much from that of the simulation approach. The errors were model have been used for designing the kanban system. In
always within 5% even for moderate size problems with 20 this paper, an approximate solution is found using neural
stages. The comparisons made in this paper were based on network meta-mdoel and then it is used as the starting point
absolute value of percentage relative errors. Instead of this in simulation to find the optimum number of kanbans of a
approach, they should have done comparisons through a manufacturing system. Actually, the word “optimum”
carefully designed ANOVA experiments. Nori and Sarkar should have been avoided in his paper, because neither
[52] have modeled the kanban system using Markov-chain the proposed meta-model nor the simulation approach will
to determine the optimum number of kanbans between give optimal number of kanbans. The optimum number of
adjacent workstations. kanbans may be called as the minimum number of kanbans.
Deleersnyder et al. [14] have modeled a blocking In this context, an attempt has been made by
situation in the queues of the kanban system using discrete Shahabudeen et al. [75] to set the number of kanbans as
time Markovian chain to study the effect of number of well as lot size at each station using simulated annealing
kanbans, machine reliability, processing time and demand algorithm. A simulation model with a single-card system
variability. Markham et al. [28] formed a procedure based has been designed and used in the analysis. A bi-criterion
rule induction approach for determining the number of objective function comprising of mean throughput rate and
kanbans and other factors in JIT. They applied classifica- aggregate average kanban queue, has been used for
tion and regression tree (CART) technique to generate the evaluation. In another work of them (Shahabudeen and
production rule, based on decision trees. This system Krishnaiah [74]), they have set the number of production

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kanbans and withdrawl kanbans at each workstation, and optimal JIT buffer level is determined from a cost analysis
lot size using genetic algorithm (GA). The solution of the using trade-off between the holding cost per unit of time
genetic algorithm is found to be better than the random and the shortage cost per unit time such that their sum is
search procedure. They concluded that the genetic algo- minimized (Salmark et al. [68]).
rithm gives better solution for the assumed kanban system.
A paper by Royce O. Bowden et al. [66] describes the
use of evolutionary programming (EP) integrated with a 6.3 Variability and its effects
simulation model of manufacturing system to determine
the minimum number of kanbans and corresponding Mehmet Savsar et al. [48] studied a simulation model to
production trigger values required to meet the demand. In investigate the effect of different operational conditions,
this paper, the inference is drawn for each measure, based including kanban withdrawal policies on three perfor-
on single replication under each solution-technique. The mance measures of JIT, viz., average throughput rate,
authors could have designed a single factor ANOVA average station utilization and average work-in-process.
experiment for each measure in which “Solution Tech- Unlike other simulation studies that use exponential or
nique” as the factor, with desirable number of replications truncated normal distribution, this model uses Erlang and
to obtain reliable inference of their simulation study. Gama distribution. It is observed that the throughput rate as
Christos G. Panayioton et al.[9] have developed a simu- well as the average station utilization is significantly
lation based algorithm for determining the minimum affected by the variability in processing time and demand
number of kanbans in a serial production system in order intervals. They proposed two types of kanban withdrawal
to maximize the throughput rate and minimize work-in- cycles, namely fixed withdrawal policy and variable
process inventory. The finite perturbation analysis (FPA) withdrawal policy. Under the fixed withdrawal policy, the
technique was used in the simulation and to get sensitivity time interval between consecutive visits of a part-carrier to
results. They have considered single product in the a workstation for kanban removal is fixed, but the order
production line. But, in most of the cases, production quantity (number of kanbans carried) is variable whereas
lines will be manufacturing multi-products. The assump- under the variable withdrawal policy, the time interval
tions of arbitrary arrival and service process distributions between consecutive visits of a part-carrier to a workstation
limit the scope of application of this paper in practice. for kanban removal is variable, but the order quantity is
fixed. As an extension of this work, the effects of different
combinations of the two kanban withdrawal policies and
6.2.5 Cost minimization model number of kanbans between workstations, on the perfor-
mance measures can be compared.
Ohno et al. [53] proposed an algorithm to determine the Huang et al. [24] have found that overtime required will
optimal number of kanbans for each of the two kinds of be increased when the variation in processing time is
kanban (production ordering and supplier kanbans) under increased. Also, they emphasized that a kanban system
stochastic demand. An algorithm was devised for deter- would not be effective with high variable processing or set
mining the optimal number of kanbans that minimizes the up time. Villeda et al. [89] performed a simulation study for
expected average cost per period. Since, no safety stock is a final assembly consisting of “3 sub-assembly lines and 4
assumed in this paper, this can be regarded as a procedure stages” repetitive production systems with kanbans. They
for determining the safety stock also. Sarkar et al.[72] concluded that improved productivity obtained through
studied a multi stage kanban system for short life-cycle unbalancing the processing time at all workstations
product in the market. In this research, the problem is to increases directly with the variability in the final assembly.
find optimally the number of orders for raw-materials, Chaturvedi and Golhar [7] simulated a kanban based flow
kanbans circulated between workstations, finished goods production line for a product in nine sequentially arranged
shipments to the buyers, and the batch size for each workstations. They observed that the system performance
shipment (lot) with minimum total cost of the inventory. A was worst for exponential processing time distribution and
cost function was developed based on the costs incurred for variability affected station utilization, throughput time and
the raw materials, the work-in-process and the finished WIP inventory. Yavuz and Satir [98] have studied the
goods. The optimal number of raw material orders that simulation of multi-item, multi-stage flow line operating
minimizes the total cost is obtained first, which is then used under the JIT philosophy with a two-card kanban tech-
to find the minimum number of kanbans, finished goods nique. The flow line produces four products through five
shipments, and the batch sizes of shipments. This paper stations. This study uses partial factorial design for
discusses a stage-wise optimization. Instead, a fully experimentation. Seven experimental clusters are designed,
integrated approach may be followed. Further, this paper each composed of at most three factors. The F ratios and
considers single product, with constant production rate at the degrees of freedom of the model are obtained from
each workstation in a serial production line. So, the work multi-variate analysis of variance (MANOVA). They found
may be extended for multi-product with varying production that decrease in lot size reduces mean length and waiting
rate at each workstation in an assembly-type production. times in work-in-process points at all kanbans levels. An
During preventive maintenance, a JIT buffer is needed increase in the uncertainty of demand arrival rates and
so that the normal operation will not be interrupted. The demand sizes increases the probability of sudden over-

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loading. An increase in the coefficient of variation in as-needed basis only, and production begins only when
processing times brings about higher line utilization and a requested. It is supposed to match customer demand, that
decrease in throughput rate. The scheduling rules tested is, producing only enough to replenish what the customer
in this paper are found to yield no significant differences in has used or sold.
the utilization of line and on the behaviours of work-in- F. Elizabeth Vergara et al.[18] have dealt the co-
process. Feeder lines may be introduced into the pull ordination between different parts of simple supply chain.
system configuration, where lines feed the final assembly Materials should be moved from one supplier to other
line. Further, alternate operating routes for the products supplier as per the JIT. For this, an evolutionary algorithm
along the line may be introduced. was used which identifies the optimal or near optimal,
synchronized delivery cycle time and suppliers’ compo-
nent sequences for a multi-supplier, multi-component
6.4 CONWIP simple supply chain. The evolutionary algorithm also
calculates a synchronized delivery cycle time for the entire
CONWIP is a kanban system working with constant work- supply chain, the cumulative cost throughout the supply
in-process. CONWIP is a generalized form of kanban. chain, and the cost to each supplier. The results of this
Like, kanban system, it relies on signals, which could be algorithm were compared with enumeration method and
electronic and it is equivalent to kanban cards. In a found that the evolutionary algorithm gives better solution
CONWIP system, the cards traverse a circuit that includes in quick manner. This algorithm uses only two-point
the entire production line. A card is attached to a standard crossover genetic operators. A third genetic operator may
container of parts at the beginning of the line. When the be introduced to further improve the performance of the
container is used at the end of the line, the card is removed evolutionary algorithm. The evolutionary algorithm may
and sent back to the beginning of the line where it waits in a be modified to handle complex supply chain problem.
card queue to eventually be attached to another container of Stefan Minner [80] did a comprehensive review of
parts. multiple-supplier inventory models in supply chain
Oscar Rubiane et al. [55] have reviewed the literatures management. SCM discusses strategic aspects of supplier
and presented the benefits and comparison of the CONWIP competition, operation flexibility, global sourcing and
systems. Most of the articles reveal that the CONWIP inventory models. Further it was extended to logistics
system works more efficiently than the conventional and multi echelon system. The emerging importance of E-
kanban systems. Yang and Kum Khiong [96] compared 3 business, especially E-procurement possibilities with the
different systems viz., Single Kanban, Dual Kanban and use of Internet technologies reduces transaction costs for
Conwip. The results show that CONWIP consistently supplier search and order placement with several suppliers
produces the shortest mean customer waiting time and and therefore multiple-supplier models are more attractive
lowest total work-in-process. Spearman et al. [79] have when compared to single sourcing alternative. This type of
stressed that the flexibility of CONWIP system allows it to market with spot offers, continuously changing suppliers
be used by any product-line where the utility of kanban and high uncertainty with respect to lead-time and
system is limited. Hence, the superiority of CONWIP pull reliability of supplies, makes multiple-supplier replenish-
system is an alternative to kanban system. They present ment and inventory strategies outperform single sourcing
theoretical arguments and simulation study of CONWIP. policies. Matheo et al. [47] have carried out a case study on
Christelle Duri et al. [8] have analyzed CONWIP inventory management in a multi-echelon spare parts
system, which consists of three stations in series. When a supply chain. This paper clearly shows the close relation-
finished part is consumed by a demand, a raw part is ship between supply chain structure and demand patterns.
released immediately and gets processed at each station The problems of managing supply chain with various
sequentially. The processing at each station does not numbers of echelons, multi model, extremely variable
always meet the requirement of quality. Hence, at the end demand and lack of visibility over the distribution channel
of processing in a station, the part is checked for quality are discussed. They provided an algorithmic solution
and if it is not as per the standard, then it is sent back to the through the comprehension of the sources of demand
same station for reprocessing. They proposed an analytical variability and through a probabilistic forecast and inven-
method to evaluate the performance of this kind of system. tory management. Isreal David et al. [29] have enumerated
In this paper, only three stations in series are considered. the vendor-buyer inventory production models. They argue
As an extension, a CONWIP system with generalized, that there should be a certain degree of independence
n stations in series may be analyzed. between successive links of the supply chain, to allow
flexibility in production management in individual links.
They identified the degree of independence and level of
6.5 SCM flexibility in terms of lot sizing and delivery scheduling in a
single-vendor-single-buyer system. In these lines, appro-
There are number of articles in SCM (Supply Chain priate two-sided vendor-buyer inventory production mod-
Management). In this present survey, a few JIT-SCM els are formulated and analyzed.
related articles are reviewed. In pull production manage- In all the papers, simulation as well as meta-heuristics
ment systems such as JIT, deliveries must be made on an can be used as powerful tools to derive results under

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probabilistic conditions. Future research can be focused in under various stable-demand conditions. The performance
these lines. of this system shows superior result.
Philipoom et al. [60] have done a kanban design with
flexible Toyota’s system. This system can dynamically
6.6 Special cases adjust the number of kanbans at each workstation in an
unstable production environment according to need/
Sarah M. Rayan et al. [69] have defined POLCA system. demand and lead time to reduce the cost. The work of
POLCA stands for Paired Cell Overlapping Loop of Cards Tardif et al. [85] introduces a new adaptive kanban-type
with Authorization. This system assumes that the factory pull control mechanism which determines the timings to
has been partitioned into non-overlapping manufacturing release or reorder raw parts based on customer demands
cells. POLCA maintains constant WIP (like CONWIP) and inventory back orders. In the adaptive pull system, the
between every pair of cells that experiences inter cell part number of kanbans in the system is dynamically readjusted
movement. Part release to a cell requires an appropriate based on current inventory level and backorder level.
kanban cards as well as an authorization from the factory Unlike a conventional system, this system absorbs extra
loading system. CONWIP and POLCA achieve a better kanbans according to the variability in demand. It was
trade-off between total WIP and total throughput time than found from the results of a simulation study of a single-
that of other systems. Their application of single chain stage, single product kanban system that these systems are
analysis for multiple chain operation raises an open beneficial in production line under variable demand
question whether a single WIP level should be maintained conditions. It shows that this adaptive system under such
for all products or individual levels for each product. conditions outperforms the traditional kanban pull control
Further, most of the studies use simulation. Hence, future mechanism. This adaptive approach may be extended for
research should be directed to develop improved search multi-stage, multi-product kanban system.
procedures for finding WIP levels in kanban systems. Kutc So [40] presents the buffer allocation problem with
Krieg et al. [38] considered a kanban controlled the objective of minimizing the average work-in-process
production system with 3 or more different products subject to minimum required throughput rate and constraint
processed on a single manufacturing facility as a decom- on the total buffer space availability. Both the balanced and
posed system. The customers for a product arrive as per unbalanced lines were considered in this work. On the basis
poisson distribution. The service time and set-up changes of empirical results, he developed a good heuristic for
are product specific and follow exponential distribution. If selecting the optimal buffer allocations. The mathematical
the customer’s demand cannot be met from stock, the model discussed in this paper is based on the two
customer leaves and satisfies his demand elsewhere (lost assumptions that there are always materials available for
sales). The production run continues until the target processing at the beginning of the line and that the last
inventory level given by the kanbans for the product has station can never be blocked. But one can assume that the
been reached. Then the manufacturing facility is set-up for first station can start processing when there are jobs
producing the next product. The basic principle of the waiting which arrived as per poisson arrival pattern
decomposed method is to decompose the original multi- (make-to-order environment). In contrast, one can consider
product system into a set of single-product subsystems (one the situation where there is finite buffer after the last
for each product). Each subsystem is modeled approxi- station where the finished items are consumed by demands
mately by a continuous-time Markov chain. In this buffer with poisson pattern (make-to-stock environment). Un-
allocation problem, the objective is to minimize the fortunately, in either case, the resulting Markov chain has
average work-in-process subject to a minimum required infinite number of states. So, one can develop simulation
throughput and a constraint on the total buffer space. The model as the last resort for studying the problem under
results of the decomposition method are compared with these two environments.
that of exact method and simulation. The key performance Lai et al. [41] have proposed a system dynamic (SD)
measures are with small relative errors for the decompo- methodology for studying the new generation of JIT in
sition method. As an extension to this work, a decompo- electronic commerce environment. It is a framework for
sition algorithm can be developed for multi-product thinking about how the operating policies of a company
kanban systems with state dependent setups. Takashashi and its customers, competitors and suppliers interact to
et al. [84] have proposed a decentralized reactive kanban shape the company’s performance over time. The system
system for multi-stage production and transportation dynamic is a study which deals with the information feed-
system with unstable changes in product demand. In the back and its evolution into future decision making. It
proposed system, the time series data of the demand from provides a new analysis of logistics policies of the
the succeeding stage are monitored at each stage company. Future work can be on extending the variables
individually and unstable changes in the demand are and elements and to conduct experiments to investigate the
detected by utilizing control charts. In order to develop a stability of the system under various conditions such as the
control rule of the buffer size, the multi-stage production sudden increase in demand and random demand, experi-
and transportation system is decomposed into single-stage mentation on the system behaviour of different types of
processing systems and the performance of the decom- customer and modes of manufacturing.
posed system is investigated by simulation experiments

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The papers by Yannick Frein et al. [97] and Yves Dallery The relationship between implementation of TQM, TPM
et al. [100] introduce a new mechanism for the coordination and JIT will lead to improvement in the manufacturing
of multistage manufacturing system called extended kanban performance (Kribty et al. [37]). Further Huang [23]
control system (EKCS). It depends on two parameters per discusses the importance of considering the integration of
stage viz., the number of kanbans and base stocks of finished TPM, JIT, Quality control and FA (Factory Automization).
products. This EKCS is evolved from combining classical Imai [27] believes that TQM and TPM are the two pillars
kanban and base stock control system. The advantages of the supporting the JIT production system. Kakuro Amasaka
extended kanban control system were compared with the [32] proposes a new JIT management system, which helps
generalized kanban control system (GKCS). It was found to transfer the management technology into management
that the capacity of EKCS depends only on the number of strategy.
kanbans but not on the base stock of finished parts. Fullerton et al. [65] have conducted a study in 253 firms
A work done by Chan [6] describes the practical in USA to evaluate empirically whether the degree with
approach to determine the optimal kanban size using which a firm implements the JIT practices affects the firms
simulation. The research was done basically for single financial performance. From their study, JIT manufacturing
product and multi product manufacturing environments in system will reap sustainable rewards as measured by
two types of JIT production systems, the pull-type and improved financial performance. Also, they studied the
hybrid-type. Their measures of performance obtained benefits of JIT implementation in 95 firms in USA. They
through simulation models are compared. For a single have concluded that JIT implementation improves the
product, when there is an increase in kanban size, the fill performance of the system, because of resultant quality
rate decreases whereas with both in-process-inventory and benefits, time based benefits, employees flexibility,
the manufacturing lead time increase. For multi-products accounting simplification, firms profitability and reduced
manufacture, when there is an increase in kanban size, inventory level.
there is an increase in the fill rate with a decrease in the
manufacturing lead-time. Leyuan Shi and Shulimen [43]
have presented a hybrid algorithm for buffer allocation 8 Conclusion
problem called the hybrid nested partition method (NP) and
tabu search method (TS). The nested partitioned method is The growing global competition forces many companies to
globally convergent and can utilize many of the existing reduce the costs of their inputs so that the companies can
heuristic methods to speed up its convergence. In this have greater profit margin. There are considerable
paper, tabu-search is incorporated in the nested partitioned advancements in technology and solution procedures in
framework and it was found that such incorporation results reality, to achieve the goal of minimizing the costs of
in superior solutions. The new algorithm is efficient for inputs. JIT-KANBAN is an important system, which is
buffer allocation problems in larger production lines. The used in production lines of many industries to minimize
nested partitioned method can be enhanced by incorporat- work-in-process and throughput time, and maximize line
ing any one or a combination of the many other heuristics efficiency. In this paper, the authors have made an attempt
viz., elaborate partitioning, sampling, backtracking to review the state-of-art of the research articles in the area
scheme, simulation, etc. Then, they can be applied to “JIT-KANBAN system”. After a brief introduction to push
combinatorial problems of this type. and pull systems, different types of kanban and their
operating principles, blocking mechanisms, the authors
have classified the research articles under JIT-KANBAN
7 JIT integration, implementation and benefits system into five major headings, viz., empirical theory,
modeling approach, variability and its effect, CONWIP and
Just-in-time is a manufacturing philosophy by which an JIT-SCM. Also, the authors have provided a section for
organization seeks continuous improvements. For ensuring special cases under JIT-KANBAN. This paper would help
continuous improvements, it is necessary for any organi- the researchers to update themselves about the current
zation to implement and integrate the JIT and JIT related directions and different issues under JIT-KANBAN
areas. If it is practiced in its true sense, the manufacturing system, which would further guide them for their future
performance and the financial performance of the system researches.
will definitely improve. The directions for future researches are presented below.
Swanson et al. [83] have reiterated that proper planning The flow shop as well as mixed model assembly line
is essential for implementation of a JIT manufacturing problems come under combinatorial category. Hence,
system and a commitment from top management is a pre- meta-heuristics viz., simulated annealing, genetic algo-
requisite. Cost benefit analysis is to be studied initially with rithm and tabu search may be used to find solution to
the knowledge of key items such as the cost of conversion determine the minimum number of kanbans and other
to a JIT system and time period of conversion. Cook et al. measures. In simulated annealing algorithm, researchers
[11], in their case study for applying JIT in the continuous can aim to device a better seed generation algorithm which
process industry, show improvements in demand forecast will ensures better starting solution. In most of the papers,
and decrease in lead-time variability. 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 reliable or a combination of the many other heuristics viz.,
inferences. elaborate partitioning, sampling, backtracking scheme,
Under batch processing system with multiple product simulation, etc. Then, they can be applied to combinatorial
types, research may be directed to study the effect of problems of this type.
different combinations of probability distributions for Ants colony optimization algorithm is a recent inclusion
arrival process and processing times on the average to the existing meta-heuristics viz., simulated annealing
inventory level of each product as finished goods. algorithm, genetic algorithm and tabu search. So, a
As an extension to the work of Markham et al. [28] on a researcher can study the solution accuracy as well as
procedure based rule induction approach for determining required computational time of this algorithm for his/her
the number of kanbans and other factors in JIT, develop- JIT problem of interest, which falls under combinatorial
ment of knowledge acquisition for this domain will be a category and compare its results with the results of the
significant contribution to literature. other three heuristics (meta-heuristics).
Sarkar et al. [72] did stage-wise optimization for a multi
stage kanban system for short life-cycle product in the Acknowledgement The authors thank the unanimous referees for
market. This work may be extended for multi-product with their constructive criticisms, which helped them to improve the
content and presentation of this review paper.
varying production rate at each workstation in an
assembly-type production.
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