Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol69.Issue1.2001:
Int. J. Production Economics 69 (2001) 39}48
Performance analysis of a batch production system with limited
#exibility
A. Claudio Garavelli*
Universita% di Lecce, Via Monteroni, 74100 Lecce, Italy
Received 6 April 1998; accepted 23 March 2000
Abstract
The concept of limited #exibility, originally applied to production planning, has shown that many bene"ts of totally
#exible systems can be obtained by less #exible systems. This concept is here applied to shop #oor control. Limited
#exibility is considered as a particular routing #exibility that allows system resources to process some products according
to a logic chain, as an intermediate con"guration between a totally #exible system (where every resource can process
every product) and a non-#exible system (dedicated resources). A simulation study is carried out to measure the system
performance such as the lead time and work-in-process for di!erent system con"gurations, with variable demand, setup
times and processing times. ( 2001 Elsevier Science B.V. All rights reserved.
Keywords: Limited #exibility; Batch production; Performance; Simulation
1. Introduction
The increase of customer requirements, for instance, in terms of demand variability and di!erentiation, together with the sti!ening of competition,
forces many manufacturing companies to be #exible and innovative. In particular, a large number of
products variable in volumes and mix has to be
provided, without neglecting cost competition. In
fact, #exibility has to be pursued together with scale
economies, as stated by `mass customizationa [1].
The search for system #exibility, agility and versatility on the one hand, and of high volumes and
low costs on the other, have led to modi"cations in
* Correspondence address: Universita della Basilicata, Via
della Tecnica 3, 85100 Potenza, Italy.
E-mail address: garavelli@.unile.it (A. Claudio Garavelli).
the design and production activities as well as in the
supply management. For instance, design modularity and quick response are some of the devices
pursued by the manufacturing companies.
From the operations management point of view,
batch production systems are among the most interesting in the search for a competitive trade-o!
between cost and #exibility.
Job shop and cellular organization are the usual
manufacturing systems adopted to perform batch
production. In particular, in cellular manufacturing
systems (CMS) parts that have similar processing
needs are grouped into part families, and machines
that meet these needs into machine cells. When part
families can be manufactured by cells, group technology allows to reduce the setup time, throughput
time and work-in-process. In this case, technical
and economic bene"ts, such as improved productivity, part quality and operations control, arise.
0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 4 3 - 8
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A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
However, despite many performance improvements, some disadvantages (such as lower machine
and labor utilization and other performance decrease) can make the production system design and
management more complex, especially when the
environment is a!ected by high uncertainty. For
instance, demand variability and resource dependability can force management to adopt more #exible
manufacturing organizations, such as job shops,
which can better deal with uncertainty [2].
Many studies (for instance [3}5]) have addressed
the bene"ts and the costs of cellular manufacturing
systems and job shop in di!erent scenarios, for
instance, depending on the performance analyzed
(e.g., lead time and system utilization) and on the
ranges assumed by various parameters (e.g., product volume and mix variability, setup and processing time, machine dedication, batch size, loading
rule). The discrepancy between the empirical and
model-based studies on this topic has also been
stressed [6].
A combination of the typical job shop #exibility
with the cellular systems productivity has then to
be searched. Two ways, in particular, can be addressed to improve batch production system performance: designing hybrid con"gurations between
cells and job shop or increasing CMS #exibility.
Referring to hybrid production systems, many
studies on di!erent production system con"gurations have been proposed to "nd trade-o!s between
CMS and job shops (for instance [7,8]), including
`virtuala cellular manufacturing [9]. On the other
side, the CMS #exibility can be increased, for instance, by the system routing #exibility, i.e. the
ability to use alternative product routes inside a cell
(intracell #ow) or to route products to cells o!ering
the same processes (intercell #ow) [10]. Routing
#exibility allows to properly respond to a changing
environment, so that the possibility of switching
products to di!erent cells in the case of environmental changes can reduce the negative impact of
variability on the system performance [11].
Routing #exibility bene"ts can balance the cost
of additional instructions, skills, material handling,
tools and "xtures, as well as the increased setup
times and work in process of routing #exibility
implementation. However, solving demand #uctuation problems by transferring work-load from
congested resources to other less congested ones
involves some disadvantages, as it has been shown
by Ang and Willey [7]. First, extra costs of material
handling are incurred; second, work #ow simplicity
is lost and the costs of production planning and
scheduling are increased; third, the number of components being processed only by a cell is reduced
and job satisfaction associated with task identity
and task signi"cance may diminish. Ang and Willey
[7] have shown that only a limited use of intercell
work-load transfer is expected to improve shop
performance with a small increase of scheduling
e!ort.
Ideally, it is preferred that a product family is
completely processed by a cell. Since this is hard to
be accomplished for all the families, studies based
on cluster analysis, in most instances, have focused
on minimizing total intercell moves [12]. Given
that the resources associated to the route implementations are usually expensive, it is often not
economic to implement more than few routes per
product. The trade-o! between productivity and
#exibility needs then to be investigated.
In this paper, the concept of limited #exibility,
proposed in the literature in a multi-plant production planning context [13], is applied to the shop
#oor management. Limited #exibility is considered
here as a particular intercellular routing #exibility
allowing some resources (cells) to process some
product family according to a logic chain. In particular, products and resources are chained by
a minimum number of links and forming the longest close loop. This chain can provide many typical bene"ts of a totally #exible system (where every
resource can process every family, as in a job shop)
and of a non-#exible system (where every resource
is dedicated to just one family).
Di!erently from the demand assignment problem [14], focused on a static simulation study with
the optimization of resource utilization and lost
sales performance, in this paper a dynamic simulation study is carried out to measure system performance such as lead time and work-in-process for
di!erent system con"gurations, with variable demand, setup times, processing times. In particular,
the comparison between the limited #exibility and
the two extremes of totally #exible and non-#exible
system con"gurations are investigated.
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
2. The simulation model
The impact of di!erent #exibility degrees in
a batch production system is investigated by a
simulation study. The study analyzes the behavior
of a production system characterized by three different degrees of #exibility (total #exibility, limited
#exibility and no-#exibility), each corresponding to
a di!erent system con"guration. These con"gurations are investigated varying the operating level
(i.e., the ratio between the mean demand volume
and the mean production capacity) of the production system.
Each system con"guration is made of similar
resources (e.g., cells or workstations), with the same
number of resources and product families. Both
resources and product families can be considered as
`black boxesa, so that it is not necessary to make
any assumption about the composition of a family
(in terms of product mix and process variability)
and on the characteristics of the resource (single
machine or group of interconnected machines).
Once a product batch is assigned to a resource,
intercellular moves are not allowed, as in a set of
parallel manufacturing cells [15]. Batches are considered as lots of products of the same family, made
of a given number of products. The variability of
this parameter is not considered in this paper.
41
A scheme of the three di!erent system con"gurations is reported in Fig. 1, with "ve product families
and "ve resources. In each con"guration, the number of resources that can work the same product
family changes. In case a, each resource can work
only one product family, because there are no links
among the resources, which are family-dedicated.
In case b, the total #exibility (i.e. complete routing
#exibility) allows every resource to process every
product family, after the eventual correspondent
setup. Finally, in case c, a product family can be
assigned only to a limited number of resources (in
this case, only to two resources), according to the
limited #exibility principles [12]. In fact, a closed
chain between products and resources is de"ned,
with the same number of products and resources
connected with each other.
In the simulation model, product families enter
the system according to a stochastic generation
modeled by an exponential distribution. Varying
the mean rate of the arrival distribution (demand
volume) yields di!erent values of the operating
level.
The demand mix is de"ned by the assignment of
a family-code to each product batch entering the
system. This assignment is modeled by a discrete
probability distribution, where each product family
has the same probability to be selected. This
Fig. 1. Production system con"gurations.
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A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
stochastic assignment allows to have a variable
demand mix in the system, but with a constant mean.
Processing times are modeled by exponential distributions. The same mean rate is considered for all
the product families to avoid the e!ect of di!erent
mean service times on the comparison of di!erent
system con"gurations. When a product family is
assigned to a resource that can work di!erent product families, a deterministic setup time is added, if
the previous product family processed by that resource was di!erent. The service rule adopted by
a resource to process the products in its queue is the
"rst-in "rst-out.
One of the critical aspects of the system is the
dispatching rule adopted to assign each product
family to a resource. In fact, if the product families
are randomly assigned to the resources of a #exible
system, setup penalties can signi"cantly a!ect the
system performance. To make a fair comparison
among the di!erent system con"gurations, #exibility should then be driven by more appropriate
dispatching rules, in order to limit the negative
e!ect of setups on system performance.
To this aim, the following dispatching rule is
considered. A dedicated resource is set for every
product family, so that a product batch entering the
system is assigned to its dedicated resource, unless
the product queue of that resource exceeds a given
threshold. If that threshold is reached, the batch is
assigned to the resource which can process that
product family (depending on the system con"guration), and has the shortest queue of products waiting. To show the e!ect of this rule on system
performance, di!erent values of the threshold TV
are considered (TV"3 and 10).
Besides these threshold values, main parameters
of the model are the operating level and the setup
time. Their values are chosen according to previous
studies on this argument [2,8]. The operating levels
considered are 60%, 70%, 80% and 90%. The
setup time is considered equal to 0% or 30% of the
total processing time. In addition, two dimensions
of the production system are considered, in order to
investigate if the number N of resources and product families a!ects system performance, with N"5
and 10, respectively.
The performances investigated are the average
lead time and the average work-in-process, to-
gether with the average resource utilization. To
obtain comparable results in the three di!erent
system con"gurations, the same number seed has
been introduced in all the stochastic distributions.
The e!ect of the dispatching rule (i.e. of the two
threshold values TV) has been analyzed "rst, referring to the case of a system with "ve resources and
product families (N"5), in the particular condition of negligible setup times (ST"0) (Section 3).
Then, the in#uence of the setup time is investigated,
referring to the value of ST"30% of the processing time (Section 4). Finally, some considerations
are provided in relation to the system dimension
increase to N"10 (Section 5).
3. System performance with negligible setup times:
the e4ect of the dispatching rule
If all the resources of a production system could
process any product family with no penalty in
terms of setup times for any product shift (ST"0),
no doubt that a totally #exible con"guration of
resources, allowing the assignment of any product
to any resource (in particular, to that with the
shortest queue), would provide the best system performance.
This consideration is still valid if every product
family is assigned to a dedicated resource, according to the threshold dispatching rule considered
above. In this case, however, #exible systems are
penalized by the fact that, to exploit the bene"ts of
#exibility, the products in the queues waiting to be
processed by di!erent resources have to reach at
least a threshold value to let the new products
entering the system be probably assigned to other
less loaded resources.
In Fig. 2, the average lead time (LT) and workin-process (WIP) are reported for di!erent values of
the operating level, for a system made of "ve resources and product families (N"5), with negligible setup times (ST"0) and two threshold values
(3 and 10). In these cases, due to ST"0, the average system utilization does not vary and is equal to
the operating level.
As we can see in Fig. 2, the three con"gurations are always ordered with the total #exibility
ahead, followed by the limited #exibility and the
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
43
Fig. 2. System performance with ST"0.
no-#exibility con"gurations. In particular, the performance of the non-#exible con"guration (which
can be determined analytically) is the worst one, due
to the absence of possible assignments of the products to non-dedicated resources, which does not
allow a dynamic redistribution of the work-load.
As the threshold value increases, the system performance of the #exible con"gurations decreases,
getting closer to the performance of the non-#exible
con"gurations (which does not vary), because the
system is more constrained and cannot completely
exploit its potential #exibility. It is also interesting
to stress that the performance of the limited #exibility con"guration is considerably closer to the total
#exibility one than to the no-#exibility one, despite
the low number of links allowed between the resources (just those of the chain), showing the bene"ts of this simple con"guration.
The di!erences in percentage of the lead time
performance (the same happens for the WIP) between the di!erent con"gurations and for the di!erent threshold values are reported in Table 1. These
values stress how the bene"ts of the #exibility increase with the operating level, due to a better
distribution of the work-load. In particular, the
results con"rm that the bene"ts are more evident in
the comparison between the limited #exibility con-
Table 1
Comparison of the LT performance of the TF, LF and NF
con"gurations, with ST"0
Operating level
0.60
0.70
0.80
0.90
TV"3
TF vs. LF
LF vs. NF
!5%
!22%
!8%
!41%
!12%
!71%
!19%
!130%
TV"10
TF vs. LF
LF vs. NF
!1%
!3%
!2%
!4%
!4%
!19%
!8%
!62%
!43%
!35%
!55%
!44%
!56%
!45%
TV"3 vs. TV"10
TF
!22%
LF
!18%
"guration and the no-#exibility con"guration, and
particularly for a low threshold of the dispatching
rule (TV"3), thus pointing out the advantages of
the limited #exibility con"guration.
In addition, it can be observed that the adoption
of the dispatching rule with a higher threshold
(TV"10) provides a higher penalty for the total
#exibility performance for any operating level, and
that, for higher operating levels, these penalties,
44
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
remarkable also for the limited #exibility con"guration tend to become stable in percentage.
4. The e4ect of the setup time
Of course, the condition of ST"0 is not real in
many cases, i.e. in all the situations where the setup
times are not negligible compared to the processing
times. In Fig. 3, the performance of the three system
con"gurations is reported for the various operating
levels, with ST"0.3 and the two threshold values
of the dispatching rule, TV"3 and TV"10. The
performance of the non-#exible con"guration, due
to its completely dedicated resources, is not a!ected
by the setup time.
Let us consider "rst the low threshold of the
dispatching rule, TV"3. As it can be observed
from the comparison with Fig. 2, the setup time
makes the performance of the #exible con"gurations decrease (in Table 2 the lead time comparison
is reported). In particular, Fig. 3 shows that the
total #exibility remains the best con"guration only
until the increasing operating level does not cause
a critical system congestion (which, for the operating levels considered, happens for values higher
than 80%), showing a negative reaction to demand
variations and limiting the possibility of satisfying
further demand. In fact, in correspondence of the
90% operating level the system is not in a steady
state anymore.
The limited #exibility, instead, shows a very good
reaction to the operating level increase: the system
performance is very close to that of the total #exibility up to the 70% of the operating level, while it is
the best con"guration for higher operating levels
(Table 3). Besides, for this con"guration the setup
Fig. 3. System performance with ST"0.3.
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
Table 2
Comparison of the LT performance of the TF and LF con"gurations
Operating level
ST"0 vs. ST"0.3
0.60
0.70
TV"3
TF
LF
!6% !17% !842%
!5% !14% !27%
!46%
TV"10
TF
LF
0%
0%
!42%
0%
0%
0.80
!5%
!5%
0.90
Table 3
Comparison of the LT performance of the TF, LF and NF
con"gurations, with ST"0.3
Operating level
0.60
0.70
0.80
0.90
TV"3
TF vs. LF
LF vs. NF
!4%
!14%
!5%
!19%
#85%
!26%
!36%
TV"10
TF vs. LF
LF vs. NF
!0.4% !2%
!1%
!4%
!4%
!12%
!13%
#83%
!21%
!32%
TV"3 vs. TV"10
TF
!19%
LF
!14%
!22%
!18%
penalties have a smaller e!ect on system performance, as it can be inferred from Table 2.
If we look at the system performance in correspondence of a higher threshold value (TV"10),
from Fig. 3 it can be inferred that the totally #exible
system still performs better with a lower threshold,
because, as we have seen in the previous section, it
exploits better its #exibility. But this is true only up
to the 70% of the operating level (Table 3). For
higher values of loading, in fact, the totally #exible
system performs better with a higher threshold,
because, by limiting the #exibility degree of the
con"guration, it also reduces the number of setups
and the consequent system congestion, unless the
45
operating level increases so much (more than 80%)
that the totally #exible system becomes unsteady
anyway (Table 3).
This e!ect is not relevant for the limited #exibility con"guration, which with the higher threshold
value provides the worst performance for any operating level, getting closer to the non-#exible con"guration. As with the lower threshold, it does not
collapse even at the operating level of 90%, where
its utilization becomes higher than that of the no#exibility con"guration, showing to be very robust
in relation to setup time variability.
The similarity of the con"guration performance
with TV"10 is also shown by the average system
utilization (Fig. 3). This performance, in fact, reveals that only with high operating levels (*80%)
the #exible con"gurations use their potential #exibility, distributing the work load to the resources.
For TV"3, instead, the utilization shows that the
resources are used more than the dedicated resources of the no-#exibility con"guration for a very
wide range of the demand.
The performance of the #exible con"gurations
decreases with setup times higher than ST"0.3. In
these cases, not shown here for the sake of brevity,
the totally #exible con"guration tends to collapse
sooner, i.e. for lower operating levels, and also the
limited #exibility can become unsteady, in particular if a dispatching rule with a low threshold value
is adopted. In short, the #exible con"gurations provide a better performance than that of the no#exibility con"guration in a range of operating
levels (or, equivalently, below a threshold operating
level) which decreases as the setup time increases.
The adoption of a higher threshold value could
then seem the right choice in these cases, but the
system performance gets very close to that of the
non-#exible con"guration, so that this con"guration appears more appealing, due to the simplicity
of its shop #oor control and to the correspondent
cost savings.
5. The e4ect of the system dimension
The simulation results reported in Fig. 4 are
obtained in correspondence of an increase of the
number of resources and the product families from
46
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
Fig. 4. System performance with ST"0.3, N"10.
5 to 10 (N"10), with ST"0.3. These results con"rm most of the considerations made in the previous cases (Sections 3 and 4).
In particular, the results con"rm that the limited
#exibility provides a very close performance to that
of the total #exibility for low operating levels, and
then becomes the best con"guration for higher
values of the demand. This con"guration always
performs better with a low threshold of the dispatching rule than with a high TV, since it allows to
better exploit the #exibility bene"ts, without considerably increasing the system congestion. On the
contrary, in correspondence of high demand (operating level higher than 70%), the total #exibility
con"guration needs a higher threshold of the dispatching rule (TV"10) to contrast the increasing
number of setups. As in the previous Sections, the
non-#exible con"guration provides a reference for
the performance, which is usually worse than in the
other #exible con"gurations, although it assures to
the system the steady state even in conditions of
very high demand. In particular, for the non-#exible con"guration the variation of the system size
does not involve any performance variation (except
the WIP increase), due to the absence of links
between dedicated resources which makes each resource behave as a single, independent system.
Also for the other con"gurations there are no
considerable variations. The most relevant result is
provided by the total #exibility con"guration
which, with a low threshold of the dispatching rule
(TV"3), becomes unstable at the operating level
of 80% for N"10 (Fig. 4), while it has just the
worst performance for that level for N"5 (Fig. 3).
This can be justi"ed by the increased congestion
generated by the larger #exible system, which provides more opportunities for the product batches to
"nd less loaded resources, thus increasing the setup
penalties for the system.
This consideration points out that as the system
size increases, the #exibility degree of the system
has to be limited in order to exploit the #exibility
bene"ts for a wide range of operating levels. This
can be done by adopting either a more `conservativea dispatching rule (TV"10), or the limited
#exibility con"guration. In the "rst case, with
TV"10, the similarity of the system behaviors in
the two sizes is stressed. However, the performance
of the #exible con"gurations in both systems is
closer to that of the corresponding no-#exibility
con"gurations, and the total #exibility becomes
unstable anyway for high values of the operating
level (more than 80%). In the second case, on the
contrary, the limited #exibility con"gurations allow
to exploit the bene"ts of #exibility with a low threshold of the dispatching rule (which assures better
performance than the non-#exible con"guration) in
both sizes, remaining stable even for high operating
levels (more than 90%).
6. Conclusions
A simulation study has been carried out to evaluate the behavior of a batch production system
characterized by di!erent #exibility degrees. In particular, three #exibility con"gurations have been
considered: total #exibility (as in a job-shop),
no-#exibility (as in a cellular system with no intercell move) and limited #exibility (a particular
inter-cell routing #exibility). The performance of
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
lead time, work-in-process and resource utilization
have been investigated for di!erent system operating levels, dispatching rules, setup times and system
dimensions.
The simulation results have shown that the limited #exibility con"guration usually provides excellent performance. In fact, this quite simple
con"guration is very good either in contexts where
more #exible systems are penalized, such as when
there are high setup times, system complexity
(many product families), and demand (high resource loading), or where the #exibility is particularly advantageous, such as when setup times are
negligible, the system is usually under-loaded or the
complexity is quite limited. In fact, in the former
case the limited #exibility con"guration exploits
the bene"ts of #exibility maintaining a low system
congestion, unless the context (demand, setup
times, complexity) becomes very critical. In the
latter case, it is competitive with the total #exibility
con"guration, because it partially shares the bene"ts of the work-load distribution among the system
resources and is less onerous in terms of investments and costs.
The total #exibility con"guration, able to distribute the work-load among a large number of resources, provides better performance than the other
two system con"gurations if the setup times are not
comparable to processing times. In this case, the
system succeeds in maintaining a good performance as the operating level increases. However,
with higher setup times the system #exibility yields
a system congestion as the demand increases, so
that the performance and the system capability of
satisfying the demand requirements drastically decrease. Other devices, such as more sophisticated
dispatching rules, for instance with thresholds variable with the demand requirements, should be adopted in this case to allow the totally #exible
con"guration to perform adequately. However, the
performance achievable by the system in this case
might not justify the complexity of this kind of
intervention.
On the other hand, the no-#exibility con"guration often provides the worst performance, even if
the system is capable of working in this case even
with a high level of the demand. This con"guration
could be preferred when the system operates in
47
conditions that would be tolerated by #exible
systems only by adopting complex dispatching
rules. In fact, in these situations the simplicity of the
shop #oor control might drive the con"guration
choice.
Acknowledgements
This research has been funded by the University
of Lecce, Department of Innovation Engineering.
Particular thanks are addressed to Alfredo Bottalico, who contributed to the "rst development of
the research. The author also wishes to thank Ilaria
Giannoccaro for her suggestions.
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[13] W.C. Jordan, S.C. Graves, Principles on the bene"ts of
manufacturing process #exibility, Management Science 41
(4) (1995) 577}594.
[14] V. Albino, A.C. Garavelli, Limited #exibility in cellular
manufacturing systems: A simulation study, International Journal of Production Economics 60}61 (1999)
447}455.
[15] R.L. Daniels, B.J. Hoopes, J.B. Mazzola, Scheduling parallel manufacturing cells with resource #exibility, Management Science 42 (9) (1996) 1260}1276.
Performance analysis of a batch production system with limited
#exibility
A. Claudio Garavelli*
Universita% di Lecce, Via Monteroni, 74100 Lecce, Italy
Received 6 April 1998; accepted 23 March 2000
Abstract
The concept of limited #exibility, originally applied to production planning, has shown that many bene"ts of totally
#exible systems can be obtained by less #exible systems. This concept is here applied to shop #oor control. Limited
#exibility is considered as a particular routing #exibility that allows system resources to process some products according
to a logic chain, as an intermediate con"guration between a totally #exible system (where every resource can process
every product) and a non-#exible system (dedicated resources). A simulation study is carried out to measure the system
performance such as the lead time and work-in-process for di!erent system con"gurations, with variable demand, setup
times and processing times. ( 2001 Elsevier Science B.V. All rights reserved.
Keywords: Limited #exibility; Batch production; Performance; Simulation
1. Introduction
The increase of customer requirements, for instance, in terms of demand variability and di!erentiation, together with the sti!ening of competition,
forces many manufacturing companies to be #exible and innovative. In particular, a large number of
products variable in volumes and mix has to be
provided, without neglecting cost competition. In
fact, #exibility has to be pursued together with scale
economies, as stated by `mass customizationa [1].
The search for system #exibility, agility and versatility on the one hand, and of high volumes and
low costs on the other, have led to modi"cations in
* Correspondence address: Universita della Basilicata, Via
della Tecnica 3, 85100 Potenza, Italy.
E-mail address: garavelli@.unile.it (A. Claudio Garavelli).
the design and production activities as well as in the
supply management. For instance, design modularity and quick response are some of the devices
pursued by the manufacturing companies.
From the operations management point of view,
batch production systems are among the most interesting in the search for a competitive trade-o!
between cost and #exibility.
Job shop and cellular organization are the usual
manufacturing systems adopted to perform batch
production. In particular, in cellular manufacturing
systems (CMS) parts that have similar processing
needs are grouped into part families, and machines
that meet these needs into machine cells. When part
families can be manufactured by cells, group technology allows to reduce the setup time, throughput
time and work-in-process. In this case, technical
and economic bene"ts, such as improved productivity, part quality and operations control, arise.
0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 4 3 - 8
40
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
However, despite many performance improvements, some disadvantages (such as lower machine
and labor utilization and other performance decrease) can make the production system design and
management more complex, especially when the
environment is a!ected by high uncertainty. For
instance, demand variability and resource dependability can force management to adopt more #exible
manufacturing organizations, such as job shops,
which can better deal with uncertainty [2].
Many studies (for instance [3}5]) have addressed
the bene"ts and the costs of cellular manufacturing
systems and job shop in di!erent scenarios, for
instance, depending on the performance analyzed
(e.g., lead time and system utilization) and on the
ranges assumed by various parameters (e.g., product volume and mix variability, setup and processing time, machine dedication, batch size, loading
rule). The discrepancy between the empirical and
model-based studies on this topic has also been
stressed [6].
A combination of the typical job shop #exibility
with the cellular systems productivity has then to
be searched. Two ways, in particular, can be addressed to improve batch production system performance: designing hybrid con"gurations between
cells and job shop or increasing CMS #exibility.
Referring to hybrid production systems, many
studies on di!erent production system con"gurations have been proposed to "nd trade-o!s between
CMS and job shops (for instance [7,8]), including
`virtuala cellular manufacturing [9]. On the other
side, the CMS #exibility can be increased, for instance, by the system routing #exibility, i.e. the
ability to use alternative product routes inside a cell
(intracell #ow) or to route products to cells o!ering
the same processes (intercell #ow) [10]. Routing
#exibility allows to properly respond to a changing
environment, so that the possibility of switching
products to di!erent cells in the case of environmental changes can reduce the negative impact of
variability on the system performance [11].
Routing #exibility bene"ts can balance the cost
of additional instructions, skills, material handling,
tools and "xtures, as well as the increased setup
times and work in process of routing #exibility
implementation. However, solving demand #uctuation problems by transferring work-load from
congested resources to other less congested ones
involves some disadvantages, as it has been shown
by Ang and Willey [7]. First, extra costs of material
handling are incurred; second, work #ow simplicity
is lost and the costs of production planning and
scheduling are increased; third, the number of components being processed only by a cell is reduced
and job satisfaction associated with task identity
and task signi"cance may diminish. Ang and Willey
[7] have shown that only a limited use of intercell
work-load transfer is expected to improve shop
performance with a small increase of scheduling
e!ort.
Ideally, it is preferred that a product family is
completely processed by a cell. Since this is hard to
be accomplished for all the families, studies based
on cluster analysis, in most instances, have focused
on minimizing total intercell moves [12]. Given
that the resources associated to the route implementations are usually expensive, it is often not
economic to implement more than few routes per
product. The trade-o! between productivity and
#exibility needs then to be investigated.
In this paper, the concept of limited #exibility,
proposed in the literature in a multi-plant production planning context [13], is applied to the shop
#oor management. Limited #exibility is considered
here as a particular intercellular routing #exibility
allowing some resources (cells) to process some
product family according to a logic chain. In particular, products and resources are chained by
a minimum number of links and forming the longest close loop. This chain can provide many typical bene"ts of a totally #exible system (where every
resource can process every family, as in a job shop)
and of a non-#exible system (where every resource
is dedicated to just one family).
Di!erently from the demand assignment problem [14], focused on a static simulation study with
the optimization of resource utilization and lost
sales performance, in this paper a dynamic simulation study is carried out to measure system performance such as lead time and work-in-process for
di!erent system con"gurations, with variable demand, setup times, processing times. In particular,
the comparison between the limited #exibility and
the two extremes of totally #exible and non-#exible
system con"gurations are investigated.
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
2. The simulation model
The impact of di!erent #exibility degrees in
a batch production system is investigated by a
simulation study. The study analyzes the behavior
of a production system characterized by three different degrees of #exibility (total #exibility, limited
#exibility and no-#exibility), each corresponding to
a di!erent system con"guration. These con"gurations are investigated varying the operating level
(i.e., the ratio between the mean demand volume
and the mean production capacity) of the production system.
Each system con"guration is made of similar
resources (e.g., cells or workstations), with the same
number of resources and product families. Both
resources and product families can be considered as
`black boxesa, so that it is not necessary to make
any assumption about the composition of a family
(in terms of product mix and process variability)
and on the characteristics of the resource (single
machine or group of interconnected machines).
Once a product batch is assigned to a resource,
intercellular moves are not allowed, as in a set of
parallel manufacturing cells [15]. Batches are considered as lots of products of the same family, made
of a given number of products. The variability of
this parameter is not considered in this paper.
41
A scheme of the three di!erent system con"gurations is reported in Fig. 1, with "ve product families
and "ve resources. In each con"guration, the number of resources that can work the same product
family changes. In case a, each resource can work
only one product family, because there are no links
among the resources, which are family-dedicated.
In case b, the total #exibility (i.e. complete routing
#exibility) allows every resource to process every
product family, after the eventual correspondent
setup. Finally, in case c, a product family can be
assigned only to a limited number of resources (in
this case, only to two resources), according to the
limited #exibility principles [12]. In fact, a closed
chain between products and resources is de"ned,
with the same number of products and resources
connected with each other.
In the simulation model, product families enter
the system according to a stochastic generation
modeled by an exponential distribution. Varying
the mean rate of the arrival distribution (demand
volume) yields di!erent values of the operating
level.
The demand mix is de"ned by the assignment of
a family-code to each product batch entering the
system. This assignment is modeled by a discrete
probability distribution, where each product family
has the same probability to be selected. This
Fig. 1. Production system con"gurations.
42
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
stochastic assignment allows to have a variable
demand mix in the system, but with a constant mean.
Processing times are modeled by exponential distributions. The same mean rate is considered for all
the product families to avoid the e!ect of di!erent
mean service times on the comparison of di!erent
system con"gurations. When a product family is
assigned to a resource that can work di!erent product families, a deterministic setup time is added, if
the previous product family processed by that resource was di!erent. The service rule adopted by
a resource to process the products in its queue is the
"rst-in "rst-out.
One of the critical aspects of the system is the
dispatching rule adopted to assign each product
family to a resource. In fact, if the product families
are randomly assigned to the resources of a #exible
system, setup penalties can signi"cantly a!ect the
system performance. To make a fair comparison
among the di!erent system con"gurations, #exibility should then be driven by more appropriate
dispatching rules, in order to limit the negative
e!ect of setups on system performance.
To this aim, the following dispatching rule is
considered. A dedicated resource is set for every
product family, so that a product batch entering the
system is assigned to its dedicated resource, unless
the product queue of that resource exceeds a given
threshold. If that threshold is reached, the batch is
assigned to the resource which can process that
product family (depending on the system con"guration), and has the shortest queue of products waiting. To show the e!ect of this rule on system
performance, di!erent values of the threshold TV
are considered (TV"3 and 10).
Besides these threshold values, main parameters
of the model are the operating level and the setup
time. Their values are chosen according to previous
studies on this argument [2,8]. The operating levels
considered are 60%, 70%, 80% and 90%. The
setup time is considered equal to 0% or 30% of the
total processing time. In addition, two dimensions
of the production system are considered, in order to
investigate if the number N of resources and product families a!ects system performance, with N"5
and 10, respectively.
The performances investigated are the average
lead time and the average work-in-process, to-
gether with the average resource utilization. To
obtain comparable results in the three di!erent
system con"gurations, the same number seed has
been introduced in all the stochastic distributions.
The e!ect of the dispatching rule (i.e. of the two
threshold values TV) has been analyzed "rst, referring to the case of a system with "ve resources and
product families (N"5), in the particular condition of negligible setup times (ST"0) (Section 3).
Then, the in#uence of the setup time is investigated,
referring to the value of ST"30% of the processing time (Section 4). Finally, some considerations
are provided in relation to the system dimension
increase to N"10 (Section 5).
3. System performance with negligible setup times:
the e4ect of the dispatching rule
If all the resources of a production system could
process any product family with no penalty in
terms of setup times for any product shift (ST"0),
no doubt that a totally #exible con"guration of
resources, allowing the assignment of any product
to any resource (in particular, to that with the
shortest queue), would provide the best system performance.
This consideration is still valid if every product
family is assigned to a dedicated resource, according to the threshold dispatching rule considered
above. In this case, however, #exible systems are
penalized by the fact that, to exploit the bene"ts of
#exibility, the products in the queues waiting to be
processed by di!erent resources have to reach at
least a threshold value to let the new products
entering the system be probably assigned to other
less loaded resources.
In Fig. 2, the average lead time (LT) and workin-process (WIP) are reported for di!erent values of
the operating level, for a system made of "ve resources and product families (N"5), with negligible setup times (ST"0) and two threshold values
(3 and 10). In these cases, due to ST"0, the average system utilization does not vary and is equal to
the operating level.
As we can see in Fig. 2, the three con"gurations are always ordered with the total #exibility
ahead, followed by the limited #exibility and the
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
43
Fig. 2. System performance with ST"0.
no-#exibility con"gurations. In particular, the performance of the non-#exible con"guration (which
can be determined analytically) is the worst one, due
to the absence of possible assignments of the products to non-dedicated resources, which does not
allow a dynamic redistribution of the work-load.
As the threshold value increases, the system performance of the #exible con"gurations decreases,
getting closer to the performance of the non-#exible
con"gurations (which does not vary), because the
system is more constrained and cannot completely
exploit its potential #exibility. It is also interesting
to stress that the performance of the limited #exibility con"guration is considerably closer to the total
#exibility one than to the no-#exibility one, despite
the low number of links allowed between the resources (just those of the chain), showing the bene"ts of this simple con"guration.
The di!erences in percentage of the lead time
performance (the same happens for the WIP) between the di!erent con"gurations and for the di!erent threshold values are reported in Table 1. These
values stress how the bene"ts of the #exibility increase with the operating level, due to a better
distribution of the work-load. In particular, the
results con"rm that the bene"ts are more evident in
the comparison between the limited #exibility con-
Table 1
Comparison of the LT performance of the TF, LF and NF
con"gurations, with ST"0
Operating level
0.60
0.70
0.80
0.90
TV"3
TF vs. LF
LF vs. NF
!5%
!22%
!8%
!41%
!12%
!71%
!19%
!130%
TV"10
TF vs. LF
LF vs. NF
!1%
!3%
!2%
!4%
!4%
!19%
!8%
!62%
!43%
!35%
!55%
!44%
!56%
!45%
TV"3 vs. TV"10
TF
!22%
LF
!18%
"guration and the no-#exibility con"guration, and
particularly for a low threshold of the dispatching
rule (TV"3), thus pointing out the advantages of
the limited #exibility con"guration.
In addition, it can be observed that the adoption
of the dispatching rule with a higher threshold
(TV"10) provides a higher penalty for the total
#exibility performance for any operating level, and
that, for higher operating levels, these penalties,
44
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
remarkable also for the limited #exibility con"guration tend to become stable in percentage.
4. The e4ect of the setup time
Of course, the condition of ST"0 is not real in
many cases, i.e. in all the situations where the setup
times are not negligible compared to the processing
times. In Fig. 3, the performance of the three system
con"gurations is reported for the various operating
levels, with ST"0.3 and the two threshold values
of the dispatching rule, TV"3 and TV"10. The
performance of the non-#exible con"guration, due
to its completely dedicated resources, is not a!ected
by the setup time.
Let us consider "rst the low threshold of the
dispatching rule, TV"3. As it can be observed
from the comparison with Fig. 2, the setup time
makes the performance of the #exible con"gurations decrease (in Table 2 the lead time comparison
is reported). In particular, Fig. 3 shows that the
total #exibility remains the best con"guration only
until the increasing operating level does not cause
a critical system congestion (which, for the operating levels considered, happens for values higher
than 80%), showing a negative reaction to demand
variations and limiting the possibility of satisfying
further demand. In fact, in correspondence of the
90% operating level the system is not in a steady
state anymore.
The limited #exibility, instead, shows a very good
reaction to the operating level increase: the system
performance is very close to that of the total #exibility up to the 70% of the operating level, while it is
the best con"guration for higher operating levels
(Table 3). Besides, for this con"guration the setup
Fig. 3. System performance with ST"0.3.
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
Table 2
Comparison of the LT performance of the TF and LF con"gurations
Operating level
ST"0 vs. ST"0.3
0.60
0.70
TV"3
TF
LF
!6% !17% !842%
!5% !14% !27%
!46%
TV"10
TF
LF
0%
0%
!42%
0%
0%
0.80
!5%
!5%
0.90
Table 3
Comparison of the LT performance of the TF, LF and NF
con"gurations, with ST"0.3
Operating level
0.60
0.70
0.80
0.90
TV"3
TF vs. LF
LF vs. NF
!4%
!14%
!5%
!19%
#85%
!26%
!36%
TV"10
TF vs. LF
LF vs. NF
!0.4% !2%
!1%
!4%
!4%
!12%
!13%
#83%
!21%
!32%
TV"3 vs. TV"10
TF
!19%
LF
!14%
!22%
!18%
penalties have a smaller e!ect on system performance, as it can be inferred from Table 2.
If we look at the system performance in correspondence of a higher threshold value (TV"10),
from Fig. 3 it can be inferred that the totally #exible
system still performs better with a lower threshold,
because, as we have seen in the previous section, it
exploits better its #exibility. But this is true only up
to the 70% of the operating level (Table 3). For
higher values of loading, in fact, the totally #exible
system performs better with a higher threshold,
because, by limiting the #exibility degree of the
con"guration, it also reduces the number of setups
and the consequent system congestion, unless the
45
operating level increases so much (more than 80%)
that the totally #exible system becomes unsteady
anyway (Table 3).
This e!ect is not relevant for the limited #exibility con"guration, which with the higher threshold
value provides the worst performance for any operating level, getting closer to the non-#exible con"guration. As with the lower threshold, it does not
collapse even at the operating level of 90%, where
its utilization becomes higher than that of the no#exibility con"guration, showing to be very robust
in relation to setup time variability.
The similarity of the con"guration performance
with TV"10 is also shown by the average system
utilization (Fig. 3). This performance, in fact, reveals that only with high operating levels (*80%)
the #exible con"gurations use their potential #exibility, distributing the work load to the resources.
For TV"3, instead, the utilization shows that the
resources are used more than the dedicated resources of the no-#exibility con"guration for a very
wide range of the demand.
The performance of the #exible con"gurations
decreases with setup times higher than ST"0.3. In
these cases, not shown here for the sake of brevity,
the totally #exible con"guration tends to collapse
sooner, i.e. for lower operating levels, and also the
limited #exibility can become unsteady, in particular if a dispatching rule with a low threshold value
is adopted. In short, the #exible con"gurations provide a better performance than that of the no#exibility con"guration in a range of operating
levels (or, equivalently, below a threshold operating
level) which decreases as the setup time increases.
The adoption of a higher threshold value could
then seem the right choice in these cases, but the
system performance gets very close to that of the
non-#exible con"guration, so that this con"guration appears more appealing, due to the simplicity
of its shop #oor control and to the correspondent
cost savings.
5. The e4ect of the system dimension
The simulation results reported in Fig. 4 are
obtained in correspondence of an increase of the
number of resources and the product families from
46
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
Fig. 4. System performance with ST"0.3, N"10.
5 to 10 (N"10), with ST"0.3. These results con"rm most of the considerations made in the previous cases (Sections 3 and 4).
In particular, the results con"rm that the limited
#exibility provides a very close performance to that
of the total #exibility for low operating levels, and
then becomes the best con"guration for higher
values of the demand. This con"guration always
performs better with a low threshold of the dispatching rule than with a high TV, since it allows to
better exploit the #exibility bene"ts, without considerably increasing the system congestion. On the
contrary, in correspondence of high demand (operating level higher than 70%), the total #exibility
con"guration needs a higher threshold of the dispatching rule (TV"10) to contrast the increasing
number of setups. As in the previous Sections, the
non-#exible con"guration provides a reference for
the performance, which is usually worse than in the
other #exible con"gurations, although it assures to
the system the steady state even in conditions of
very high demand. In particular, for the non-#exible con"guration the variation of the system size
does not involve any performance variation (except
the WIP increase), due to the absence of links
between dedicated resources which makes each resource behave as a single, independent system.
Also for the other con"gurations there are no
considerable variations. The most relevant result is
provided by the total #exibility con"guration
which, with a low threshold of the dispatching rule
(TV"3), becomes unstable at the operating level
of 80% for N"10 (Fig. 4), while it has just the
worst performance for that level for N"5 (Fig. 3).
This can be justi"ed by the increased congestion
generated by the larger #exible system, which provides more opportunities for the product batches to
"nd less loaded resources, thus increasing the setup
penalties for the system.
This consideration points out that as the system
size increases, the #exibility degree of the system
has to be limited in order to exploit the #exibility
bene"ts for a wide range of operating levels. This
can be done by adopting either a more `conservativea dispatching rule (TV"10), or the limited
#exibility con"guration. In the "rst case, with
TV"10, the similarity of the system behaviors in
the two sizes is stressed. However, the performance
of the #exible con"gurations in both systems is
closer to that of the corresponding no-#exibility
con"gurations, and the total #exibility becomes
unstable anyway for high values of the operating
level (more than 80%). In the second case, on the
contrary, the limited #exibility con"gurations allow
to exploit the bene"ts of #exibility with a low threshold of the dispatching rule (which assures better
performance than the non-#exible con"guration) in
both sizes, remaining stable even for high operating
levels (more than 90%).
6. Conclusions
A simulation study has been carried out to evaluate the behavior of a batch production system
characterized by di!erent #exibility degrees. In particular, three #exibility con"gurations have been
considered: total #exibility (as in a job-shop),
no-#exibility (as in a cellular system with no intercell move) and limited #exibility (a particular
inter-cell routing #exibility). The performance of
A.C. Garavelli / Int. J. Production Economics 69 (2001) 39}48
lead time, work-in-process and resource utilization
have been investigated for di!erent system operating levels, dispatching rules, setup times and system
dimensions.
The simulation results have shown that the limited #exibility con"guration usually provides excellent performance. In fact, this quite simple
con"guration is very good either in contexts where
more #exible systems are penalized, such as when
there are high setup times, system complexity
(many product families), and demand (high resource loading), or where the #exibility is particularly advantageous, such as when setup times are
negligible, the system is usually under-loaded or the
complexity is quite limited. In fact, in the former
case the limited #exibility con"guration exploits
the bene"ts of #exibility maintaining a low system
congestion, unless the context (demand, setup
times, complexity) becomes very critical. In the
latter case, it is competitive with the total #exibility
con"guration, because it partially shares the bene"ts of the work-load distribution among the system
resources and is less onerous in terms of investments and costs.
The total #exibility con"guration, able to distribute the work-load among a large number of resources, provides better performance than the other
two system con"gurations if the setup times are not
comparable to processing times. In this case, the
system succeeds in maintaining a good performance as the operating level increases. However,
with higher setup times the system #exibility yields
a system congestion as the demand increases, so
that the performance and the system capability of
satisfying the demand requirements drastically decrease. Other devices, such as more sophisticated
dispatching rules, for instance with thresholds variable with the demand requirements, should be adopted in this case to allow the totally #exible
con"guration to perform adequately. However, the
performance achievable by the system in this case
might not justify the complexity of this kind of
intervention.
On the other hand, the no-#exibility con"guration often provides the worst performance, even if
the system is capable of working in this case even
with a high level of the demand. This con"guration
could be preferred when the system operates in
47
conditions that would be tolerated by #exible
systems only by adopting complex dispatching
rules. In fact, in these situations the simplicity of the
shop #oor control might drive the con"guration
choice.
Acknowledgements
This research has been funded by the University
of Lecce, Department of Innovation Engineering.
Particular thanks are addressed to Alfredo Bottalico, who contributed to the "rst development of
the research. The author also wishes to thank Ilaria
Giannoccaro for her suggestions.
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