Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol68.Issue1.Oct2000:
Int. J. Production Economics 68 (2000) 43}57
Production variability and shop con"guration: An
experimental analysis
Andrea D'Angelo!,*, Massimo Gastaldi", Nathan Levialdi#
!Department of Production, Systems and Computer Science, University of Rome-Tor Vergata, Italy
"Department of Electrical Engineering, University of L'Aquila, Italy
#Department of Industrial Engineering, University of Perugia, Italy
Received 6 June 1998; accepted 4 February 1999
Abstract
The selection of an e$cient shop layout at manufacturing level is a strategic problem, involving considerable
immobilisation of "nancial resources. Often the problem is hierarchically solved on several levels. In the present paper we
focus on the level of physical system planning and try to de"ne the best shop con"guration in terms of process resources
layout (work centres organisation), considering di!erent variability conditions for demand (system input). Data obtained
from simulation experiments are being statistically analysed to clear the weak and strong points for each con"guration
(job shop, #exible cell shop, #ow shop). ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: Manufacturing systems; Shop layout; Simulation; Performance evaluation; Production variability
1. Introduction
The selection of an e$cient productive layout at
manufacturing level is a strategic problem, involving considerable immobilisation of "nancial
resources. Therefore, planning and design of production lines include a careful analysis [1], devoted
to individuate technological bind that the related
production imposes, factors in#uencing strategic
system performances and the opportune tool for
the selection of the best solution. Often the problem
is hierarchically resolved on several levels. In particular, the level of physical system planning requires a further distinction of physical components
* Corresponding author. Tel./Fax: #39-6-725-97358.
E-mail address: dangelo@disp.uniroma2.it (A. D'Angelo).
from the control logic. In the present paper we
focus on the "rst item: organisation of process
resources layout. The manufacturing layout choice
passes in fact through the quanti"cation of tradeo! between routing #exibility and set-up savings,
i.e., performances of alternative con"gurations
showing symmetric advantages and disadvantages.
In particular, the analysis goes over a restricted set
of alternative choices: job shop, cellular manufacturing, #ow shop and hybrid systems. The "rst type
is a process layout and involves a system resource
organisation in isolated work centres, in which
machines perform all the same type of processing
on parts to be produced. The second type of layout
is organised in isolated cells in which only a suitably scheduled product family is processed. That is,
system resources are allocated at di!erent working
departments on the basis of technological binds
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 0 1 2 - 2
44
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
imposed by production cycle of products families.
The third type of layout provides the organisation
of dedicated production #ow lines in which part
types follow obliged and unidirectional routes. Besides there exist other hybrid forms which mediate
functional features of the two types just described.
The attractiveness of each type of con"guration is
basically tied to the particular system performance
considered by management. Aim of this research is
to compare alternative productive layouts testing
their robustness with respect to system input variability. That is, we want to quantify trade-o!
between routing #exibility and set-up savings with
respect to di!erent condition of demand variability.
Such variability will be modelled through the variation coe$cient of lots inter-arrival time to the
system and through unbalancing of product mix.
Simulation experiments will be conducted with this
aim: gathering the correlation between system performances and such experimental factors testing
the relative statistical signi"cance.
The paper is organised as follows: in Section 2,
literature contributions are reviewed aiming to
underline the main topics investigated in this
research area. In Section 3, the system under study
is brie#y described, while in Section 4, the paper's
aims are presented in terms of research questions,
experimen-independent factors and the observed
dependent variable. Data obtained by simulation
experiments are analysed in Section 5 and "nally
Section 6 concludes the paper discussing the major
results and possible future research issues.
2. Relevant literature review
In literature, works that investigate dynamics
related to productive #ows in a manufacturing system agree in recognising the process layout to be
superior in terms of e$ciency performance. Morris
and Tersine [2] proved that the job shop has the
best performance in almost all experimental test
conditions. Observed system performances (mean
throughput time and the work in process inventory) are analysed with respect to a series of test
factors such as demand stability, productive #ow
direction and set-up time. Statistical analysis conducted on experimental results data shows that
shop layout organised in manufacturing cells (each
dedicated to a single part family) provides clear
advantages only for a high level of set-up time
reductions. In reality, the choice of Group Technology implementation is often linked to advantages
related to an improvement in quality and rework
costs, since workers are held accountable for a completed product rather than a completed process.
Unfortunately these results are dependent upon
human response and it is very di$cult to model
them in an analytical way. Jensen et al. [3] get the
same results considering the impact of di!erent
experimental factors such as bu!er scheduling rules
and machine customisation. In general, conclusions
of in-depth researches show that shop layout organised in manufacturing cells (which imposes the
arrangement of dedicated routes for objects processed) shows an extreme in#exibility. Suresh [4,5]
and Suresh and Meredith [6] utilised queuing
models to analyse e!ects of partitioning work
centres in a shop level investigation. Even the introduction of set-up reductions partitioning does not
show substantial bene"ts. The obtained results are
con"rmed utilising a simulation pattern which
allows to model a wider number of factors and
thereby ensure a greater reality adhesion. Once
more the attractiveness of cellular layout would
seem strongly tied to particular combinations of lot
size and set-up reduction levels. Besides, it demonstrates that a part of the functional in#exibility of
such a shop can be avoided allowing inter-cell
movements. The study of Flynn and Jacobs [7]
compares di!erent strategy for arranging machines
in a facility. Computer simulation was used to
compare process layout (the arrangement of groups
of machines where the machine within a group are
interchangeable) to cellular layout designed using
group technology concepts. Four layout strategies,
including process layout, cellular layout, and two
hybrid layouts, were compared in two machineshop models. Despite the shop using cellular
layouts had shorter setup times and shorter distances traveled, the shop with process layout had
better performance. This suggests that a wellorganized traditional job shop may be able to
achieve overall performance that at least is comparable to that of the same shop using cellular layout.
A growing amount of literature is then critical of
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
group technology and cellular manufacturing. Ang
and Willey [8] stated that group technology and
hence cellular manufacturing lacks the #exibility
necessary for coping with work-load variations.
They concluded that this choice is not always economically justi"ed in practice. Also Leonard and
Rathmill [9], although formerly proponents of
group technology, believed good process layout
would prove superior in comparison with cellular
layout shop con"guration.
In generating the part-machine matrix we do not
impose the condition of unidirectional #ow, i.e., it is
possible for a part type to be processed by a machine visited before. Such an hypothesis was considered important for a more realistic adhesion. It
introduces a complication which is partly balanced
by the extreme easiness of the part-machine matrix.
Results of the random formulation are reported in
Table 2. For each family the manufacturing cycle
sequencing is shown in terms of visited machines
(the mean value of cycle time for each operation).
As you can see, part family P undergoes six opera2
tions, while P only three. Moreover, only manu4
facturing cycle sequencing for P does not involve
4
backward processing, since its cycle provides
operations on all di!erent machines. To this purpose parts belonging to product family P have
2
a double reprocessing: its cycle involves passage on
machines 4 and 6, in two di!erent situations.
Each work centre is supplied with an input bu!er
of unlimited capacity. Parts arriving in a department to be processed are stored in such a bu!er and
are scheduled through a simple "rst-come-"rst-served rule. Each machine can process only one operation at a time, and is continuously available for
production when it is not processing a part. Two
set-ups may occur when a part is dispatched to
a machine. The "rst one represents the requested
intervention before a part was processed (10% of
mean processing time). The second one represents
the requested intervention on machine which has to
process a part belonging to a di!erent family with
respect to the last one dispatched (50% of mean
processing time). Processing times relative to machine operations are stochastically distributed in a
log-normal curve around the mean value shown in
Table 2 (variation coe$cient of such a distribution
3. System description
In the above contributions, production system
was assigned through a more or less complex partmachine matrix. Here this matrix was randomly
generated (Table 1).
To this purpose, the following assumptions were
made:
f total number of possible processing (6);
f total number of product families (4);
f minimum and maximum number of operations
expected for each product family (3}6);
f minimum and maximum value of cycle time relative to each processing for each kind of part type
(5}10 minutes).
Table 1
Part-machine matrix
P
1
P
2
P
3
P
4
WC
1
WC
2
WC
3
x
x
x
x
x
x
x
x
x
WC
4
WC
5
WC
6
x
x
x
45
x
x
x
Table 2
Family sequencing and mean processing times on each machine
Process P 1
Part family B
P
1
P
2
P
3
P
4
WC
2
WC
6
WC
4
WC
2
2
(7)
(7)
(10)
(6)
WC
5
WC
4
WC
6
WC
5
3
(5)
(9)
(6)
(6)
WC
1
WC
1
WC
4
WC
1
(7)
(8)
(10)
(6)
4
5
6
WC (9)
5
WC (7)
3
WC (8)
2
WC (10)
3
WC (8)
4
WC (8)
6
WC (6)
2
46
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
is 0.2). Moreover it is assumed that all machines are
subject to breakdown events. For each server allocated in a work centre, these events have a probability occurrence distributed in a log-normal curve
around a mean value of 1000 minutes and a standard deviation of 100 minutes. Mean time to repair,
instead, is distributed in a Gamma curve with
a mean value of 50 minutes. Parts to be produced
arrive at the system in lots of "xed size (5). In
designing work centres, cells or #ow lines, in terms
of minimum server allocation necessary to respect
the continuity bound and to avoid system parts'
accumulation, we make use of the following model
[10,11]:
4
P
n " 505 + C ¹p
j
ij
i 60;
m j/1
∀i"1,2, 6,
(1)
where P is the hourly total system input, ; is the
505
m
average expected utilisation, C is the percentage
j
mix relative to family j ( j"1,2, 4) and ¹p is the
ij
average processing time for parts belonging to family j, for operation i.
Total system load is balanced by the production
capacity of each centre, cell or line, given a certain
expected average system utilisation (; ). This varim
able quanti"es the supposed servers' availability,
which is limited by set-up and breakdown events.
The use of such a formula allows balancing system
production resources. If we suppose an average
expected utilisation ; , the number n given in Eq.
m
i
(1) represents the minimum number of servers in
work centre, cell or line i, complying with the
steady continuity bound (System Input"System
Output [12]). In its turn, the observance of such
a bound allows to examine (after an unavoidable
transient warm-up period) system variables' steady
state.
4. Experimental design
The problem of the shop manufacturing layout
design is often solved by means of simulation models which enable to test the e!ect of variability of
a few experimental factors (independent variables)
on typical system performances (dependent variables). This paper focuses on the quanti"cation of
trade-o! between routing #exibility and set-up savings, detecting the robustness of di!erent shop
layouts with respect to demand variability. Three
overall research questions are speci"cally addressed:
f Research question 1: Do di!erences in shop
layout organisation have a meaningful e!ect on
system performances?
f Research question 2: Does demand variability
have an impact on system performances?
This second point has been addressed in order to
be able to generalise results of the study to a wider
variety of shops characterised by di!erent demand
conditions.
f Research question 3: Which component of demand variability has the largest e!ect on system
performances?
The analysis will detect the robustness of shop
layouts with respect to demand variability
modelled in terms of lots inter-arrival time variability (I-a variability) and mix balancing. Hence, we
consider three independent variables (see Table 3).
Each one has three distinct levels for a total of 27
possible combinations which de"ne as much con"gurations.
Shop layout: Job shop is a process layout and
involves a system resource organisation in isolated
work centres in which all machines perform the
same type of processing on parts to be produced
(Fig. 1).
Flexible cell shop involves a layout organised in
isolated cells in which only a suitably scheduled
product family is processed (Fig. 2). That is, system
resources are allocated at di!erent working departments on the basis of technological binds imposed
by the production cycle of products families.
Table 3
Independent variables for the experimental analysis
Level Shop layout
0
1
2
Mix
I-a variability
Job shop
Balanced
Null
Flexible cell shop Medium unbalanced Medium
Flow shop
Strongly unbalanced Large
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Demand variability: Demand variability has two
main components:
f Variability of inter-arrival time of lots to the
system;
f Balancing of product mix.
The "rst factor is modelled by means of the
variation coe$cient of a log-normal distribution
ranging from 0%, 40% and 100% of mean value.
The second one is modelled by means of mix ratios
selected so that system input is divided up among
various families in accordance with a "xed balanc-
47
ing level (Fig. 3). In particular, the balancing is
a function of P load/P load ratio which ranges
1
4
from 1 to 4 to 10 (Table 4).
Considering a total system load of 20 parts per
hour and a "xed lot size of 5 parts each, the scheduling scheme is as shown in Table 5.
4.1. Simulation models
The three research questions are investigated
with reference to the manufacturing system described in Section 3. Developing a full factorial
experimental design, we consider 27 models de"ned
by all possible combinations of experimental factor
levels. These models were coded in a special software tool (WITNESS 7.0t). In de"ning simulation
experiments three issues have been considered:
f the e!ect of bias deriving from starting conditions, resulting in atypical system behaviour;
f observation time length of system events for each
simulation run;
f number of replications for each run (the experiment sample size).
Fig. 1. Job shop layout.
The "rst issue was detected by means of pilot
runs. System variables' values tracking allows to
analyse transient behaviour and to determine at
which point system status reaches steady state. To
this purpose we monitored the trend of mean #ow
Fig. 2. Flexible cell shop layout.
48
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Fig. 3. Flow shop layout.
Table 4
Demand variability, levels of mix balancing
Table 5
Scheduling scheme, mean inter-arrival time of lots to the system
Part family
Part family
P
1
P
2
P
3
P
4
Mix ratio
Product mix
Balanced
Med.
unbalanced
Strongly
unbalanced
25%
25%
25%
25%
1:1
40%
30%
20%
10%
4:1
50%
30%
15%
5%
10:1
time percentage variation as signal for detecting
bias e!ect deriving from starting conditions (use of
alternative variables such as work in process, average bu!er size etc. lead to the same results).
Fig. 4 plots the trend of this variable over the "rst
30.000 running time units (minutes). As evident in
this particular situation, plot #uctuations tend to
fade away in about 210 hours. Clearly, at that
moment, mean #ow time for manufactured parts
becomes stable around its steady value.
Other plots show the same results for other
experimental conditions, even if inter-arrival variation provides persistent and larger #uctuations
around the steady value, which is zero. In all cases,
we can assume that transient state "nishes after
P
1
P
2
P
3
P
4
Mix ratio
Product mix
Balanced
Med.
unbalanced
Strongly
unbalanced
60 min
60 min
60 min
60 min
1:1
37.5 min
75 min
50 min
150 min
4:1
30 min
50 min
100 min
300 min
10:1
250 hours (15.000 running minutes) and data collection can start after this time length. Hence, in each
simulation run, data relative to "rst 15.000 minutes
are cleared to avoid e!ects of initialisation bias.
Once the point at which system reaches the steady
state has been selected, The second issue involves
determination of total replication time length
and sample size. For this purpose, we considered
"ve replications for each experiment. This sample
size was chosen to provide not-correlated mean
values for observed events [13]. At the same time
we "xed 90.000 minutes of total running for each
experiment replication. This choice assures a duration of 1250 hours (about 30 weeks) of system
steady functioning.
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
49
Fig. 4. Lead time percentage variation over time for di!erent models.
4.2. Performance measures: Dependent observed
variables
most interesting manufacturing system performances and are abbreviated named as follows:
The dependent variables observed in steady state
system simulation running have been selected according to literature indications [14] relatively to
f LT: average lead time
f WIP: average work in process
f Buf. size: average bu!er size
50
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
f Buf. time: average time spent in bu!ers
f CV buf.: coe$cient of variation of bu!er time
spent in bu!ers
f Av. Util.: average work centres' utilisation
f CV Util.: coe$cient of variation of work centres
utilisation
Besides, we add two other variables:
f Server: minimum number of servers in system
f Volume: total system output
The "rst one is determined according to Eq. (1).
It represents the total number of servers located to
each work centre, i.e. the minimum resource necessary to respect the continuity bound and avoid
system parts' accumulation, given an average
expected utilisation. Hence, we consider this variable as a measure of performance, since its value
re#ects the ability of the system to outperform the
pre-assigned scheduled production, as a proper e$ciency index. The second one quanti"es the number
of parts shipped out from the system. Since we have
imposed a resource sizing in order to avoid parts
accumulation and observe steady state running,
such a variable is not considered as a proper performance, because at any instant the steady continuity
bound (System Input"System Output) is given. Such
a condition imposes that total production volume
will be always the same in each experimental condition, not dependent on demand variability level or
shop layout design (P , hourly total system input,
505
was imposed equal to 20 parts). However, in this
context, we consider this variable, since it will be
very interesting to verify the veracity of considerations just exposed.
4.3. Other model assumptions
Utilised simulation models do not contemplate
time consuming computation for material handling
or inter-machine movements. Given the model
assumptions, we suppose, in fact, that time shift due
to parts movement does not impact system performances in a signi"cant manner. In reality the
weight of move time on system performances (in
particular on #ow time and queue related variables)
can be very di!erent for di!erent shop layout or-
ganisations. In other words, part of the di!erence
observed in lead time performance between job
shop and #ow shop, can be explained in terms of
move time of parts along manufacturing lines. But
such an e!ect is not numerically relevant with
respect to others, and in particular for those interested in the aim of our research. Hence, we assume
zero move time. Relevant components of time
dependent variables are those due to set-up, machine processes and in queue waiting.
5. Results analysis
Experimental results of 27 simulation models,
conducted to quantify the e!ect of independent
variables on system performances, are shown in
Table 6.
To answer research questions presented in Section 4, "rstly we proceed analysing results aiming
to discover possible correlation between observed
independent variables. So the correlation matrix
for nine system performances was built in order to
check the presence of a possible common trend.
Such an analysis is shown in Table 7.
Choice of such a functional expression for e$ciency index is justi"ed considering the positive
e!ect of average system utilisation (Av. Util.) and
the negative one of utilisation variation coe$cient
(CV Util.) and total number of system servers (Server) on global performance. As expressed above, in
fact, a manufacturing system is considered e$cient
if the resource needed to process a given production
volume is minimal (minimum Server), and if system
work-load is well balanced between work centres
(minimum CV Util, maximum Av. Util.). With reference to the agility index it has been considered
the negative e!ect of queue related performances
(WIP, Buf. size, Buf. time), and of lead time.
A manufacturing system is considered agile or lean
if bu!ers content relains limited on time. This condition avoids system congestion, reduces production locking up and parts #ow times. Besides, in
both cases, expressions involve constants for
numerical normalisation of observed values.
Coe$cients in the matrix highlight a strong correlation between two distinct clusters of variables
(pointed out in bold). Once identi"ed, such clusters
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
51
Table 6
Mean values for observed dependent variables for each experimental model
Run
Independent variables
Dependent variables
Layout
X
1
Mix bal. I-a. Var. Volume
X
X
Y
2
3
1
WIP
Y
2
LT
Y
3
Buf. size Buf. time CV buf.
Y
Y
Y
4
5
6
Av. Util. CV Util. Server
Y
Y
Y
7
8
9
1
2
3
4
5
6
7
8
9
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.981
25.130
24.988
24.994
25.030
24.886
25.001
24.873
25.086
49.85
61.31
110.98
41.69
46.29
86.41
40.63
43.47
102.79
149.23
182.28
329.73
124.84
138.34
259.42
121.66
130.87
304.69
5.48
7.40
15.73
3.90
4.68
11.45
3.72
4.23
14.15
27.51
37.10
78.44
14.65
17.52
42.07
14.12
16.46
55.97
147.89
155.02
150.82
70.37
68.25
56.23
85.95
90.36
111.25
60.14
60.51
60.05
62.14
62.22
61.94
61.59
61.26
61.82
8.53
8.69
8.30
8.63
8.62
8.74
9.68
9.64
9.84
20
20
20
21
21
21
22
22
22
10
11
12
13
14
15
16
17
18
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.999
24.956
24.798
25.010
24.955
24.816
24.997
24.955
24.867
32.63
35.03
57.98
32.03
36.33
57.99
29.47
32.78
48.97
97.74
105.10
174.39
95.88
109.02
174.52
88.28
98.41
147.28
1.84
2.12
4.72
1.66
2.15
4.60
1.37
1.76
3.58
12.02
13.48
27.87
10.78
13.54
28.80
8.64
11.25
22.95
72.32
65.52
82.84
74.64
76.93
89.13
87.56
97.21
108.63
56.54
56.52
56.16
57.07
56.93
56.57
55.96
55.85
55.69
11.35
11.22
11.98
14.34
14.29
14.42
15.82
15.90
15.55
21
21
21
23
23
23
24
24
24
19
20
21
22
23
24
25
26
27
2
2
2
2
2
2
2
2
2
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.987
24.995
25.021
25.015
25.048
24.983
25.014
25.061
24.754
38.35
42.83
64.45
38.05
43.21
66.08
29.80
33.24
46.60
114.90
128.18
192.17
113.85
129.10
197.33
89.19
99.25
140.86
1.27
1.51
2.65
1.19
1.46
2.67
0.74
0.92
1.63
15.26
18.02
31.71
13.64
16.30
29.48
9.08
10.90
18.32
74.02
79.70
87.06
91.26
96.78
124.54
116.60
116.22
152.27
51.74
51.80
51.69
49.57
49.66
49.49
43.12
43.22
42.72
19.04
19.44
19.10
32.31
32.40
32.41
39.70
39.52
39.50
24
24
24
26
26
26
29
29
29
Table 7
Correlation matrix for observed dependent variables
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
Volume
WIP
LT
Buf. Size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
1.000
0.002
1.000
!0.009
1.000
1.000
0.066
0.922
0.922
1.000
0.042
0.974
0.973
0.925
1.000
0.070
0.328
0.326
0.271
0.424
1.000
0.078
0.357
0.357
0.582
0.360
!0.59
1.000
!0.010
!0.307
!0.308
!0.519
!0.339
0.294
2 0.958
1.000
!0.063
!0.366
!0.366
!0.556
!0.412
0.213
2 0.927
0.961
1.000
52
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
are de"ned in a synthetic way through an e$ciency
and agility index. Their numerical expression is
underlined in Table 8.
The statistical regression analysis allows to check
the signi"cance of experimental factors with reference to their in#uence on system performances,
considering the hypotheses of a linear dependence.
In Table 9 values of multiple correlation coe$cient
R2 (columns 2}4) are listed for rating of "rst order
e!ect for each di!erent shop layout con"guration.
In column 5, R2 value is presented for all the e!ects,
up to third order, while in column 6 the ratio is
relative to the percentage due to "rst order terms.
Variance explained by experimental factors at "rst
order level is rather high in cases highlighted in
bold. In job shop and #ow shop cases volume seems
not to be a!ected by experimental factors' variations. Also the ANOVA test (F-value in column 7)
strengthens such results. This observation is e!ective for average utilisation too, for two of the three
shop layouts.
The analysis clearly indicates that the impact of
Inter-arrival time variation mainly regards the agility performances (i.e. lead time, work in process,
average bu!er size, average time spent in bu!er),
while the impact of mix balancing variation mainly
regards the e$ciency performances (i.e. number of
servers in the system and variation coe$cient of
resource utilisation).
Through a Student T-test, statistical results analysis allowed to highlight the presence of meaningful
di!erences in performance mean values, observed
Table 8
Components and numerical expressions for e$ciency and agility
indexes
Components Expressions
Cluster 1: Ezciency z
z
Cluster 2: Agility
Av. Util.
CV Util.
z
Server
z
z
WIP
LT
z
z
Buf. size
Buf. time
100 ) Av.Util.
CVUtil. ) Server
106
WIP ) LT ) Buf.size ) Buf.time
in groups corresponding to the experimental factor
level combinations. Preliminary F-test was
performed to test the homogeneity of means for
di!erent groups examined, by means of sample
data analysis. In Table 10 Student T-test data are
listed. Almost all di!erences observed by varying
shop layout level are signi"cant at an a level of
about 0.05 (bold values). We cannot have the same
statement for mix balancing factor, while relatively
to inter-arrival time variation we observe a signi"cant di!erence in system behaviour (particularly for
queue related performances), for groups of 0 and
2 levels. That is, only for a remarkable increase in
inter-arrival time variability (variation coe$cient
ranging from 0% to 100%), a real worsening of
system agility can be stated.
In curves shown in Figs. 5}8 the e!ects whose
statistics are signi"cant, are plotted with reference
to di!erent experimental conditions.
Queue related performances deteriorate against
inter-arrival time variability increase (Fig. 5). Job
shop layout shows the worst levels, while #exible
cell shop the best ones. The plotted trend is the
same for the lead time variable (Fig. 6), but in this
case the unbalancing #attens curves of #exible cell
shop and #ow shop layouts.
Fig. 7 shows that, apart from system input
variability, the choice of a shop layout with dedicated resources involves a decrease in system e$ciency, in terms of average work centres utilisation.
Besides, the curve has a rather similar trend for
di!erent mix balancing conditions (plots not presented here). The only experimental factor having
a strong e!ect on such a variable is hence the shop
layout design.
Instead, in terms of mix balancing variability, the
only e!ect supported by statistical indexes is relative to the dependent variable named server. Mix
unbalancing implies an increase of resource needed
in work centres (Fig. 8). In fact it involves an increase in the presence of parts characterised by
longer sequences and longer processing times.
Hence, the most evident e!ect is the increase of
system load, with consequent need for additional
servers, as established in Eq. (1).
Finally, let us consider the data shown in
Table 11. Relative to aggregate indexes de"ned
above as e$ciency and agility, mean values are
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
53
Table 9
Multiple regression coe$cient (R2) and test-F for each observed variable
Mix. bal.
I-a. var.
I order
Tot.
% I order
(a) Job shop
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
0.0584
0.0334
0.0333
0.0401
0.1356
0.3843
0.4484
0.8116
1.0000
0.0009
0.7595
0.7630
0.7530
0.6139
0.0028
0.0001
0.0001
0.0000
0.0593
0.7929
0.7963
0.7931
0.7495
0.3870
0.4485
0.8117
1.0000
0.1618
0.9776
0.9803
0.9787
0.9642
0.9710
0.9505
0.9756
1.0000
0.3666
0.8111
0.8123
0.8103
0.7773
0.3986
0.4719
0.8320
1.0000
0.1892!
11.4860
11.729
11.4983
8.9745
1.8942!
2.4398!
12.9283
d
E$ciency
Agility
0.8732
0.2452
0.0000
0.5778
0.8732
0.8230
0.9894
0.9749
0.8825
0.8442
20.6522
13.9473
(b) Flexible cell shop
Volume
0.0140
WIP
0.0332
LT
0.0327
Buf. size
0.0475
Buf. time
0.0384
CV buf.
0.6134
Av. Util.
0.2635
CV Util.
0.9498
Server
0.9643
0.8853
0.7995
0.8010
0.7941
0.8024
0.2463
0.1202
0.0011
0.0000
0.8993
0.8327
0.8336
0.8416
0.8407
0.8597
0.3837
0.9509
0.9643
0.9873
0.9913
0.9912
0.9915
0.9909
0.9699
0.9909
0.9948
1.0000
0.9109
0.8401
0.8411
0.8488
0.8484
0.8864
0.3872
0.9558
0.9643
26.7810
14.9329
15.0342
15.9341
15.8362
18.3870
1.8677!
58.1273
81.0000
E$ciency
Agility
0.9296
0.1263
0.0030
0.7480
0.9326
0.8743
0.9957
0.9863
0.9366
0.8865
41.5118
20.8710
(c) Flow shop
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
0.0771
0.1667
0.1628
0.2040
0.2478
0.6873
0.9211
0.9720
0.9868
0.1669
0.6478
0.6548
0.6263
0.6000
0.2218
0.0004
0.0000
0.0000
0.2440
0.8146
0.8177
0.8303
0.8478
0.9092
0.9215
0.9720
0.9868
0.7855
0.9840
0.9855
0.9850
0.9876
0.9746
0.9998
0.9999
1.0000
0.3106
0.8278
0.8297
0.8429
0.8584
0.9329
0.9216
0.9721
0.9868
0.9680!
13.1791
13.4544
14.6768
16.7052
30.0310
35.1925
104.0524
225.0000
E$ciency
Agility
0.9422
0.3400
0.0000
0.3673
0.9422
0.7073
0.9998
0.9594
0.9425
0.7372
48.9354
7.2481
F-value
! Not signi"cant.
shown for di!erent combinations of experimental
factors.
Histograms built on such data are plotted in
Figs. 9}11. With reference to research question
1 expressed in Section 4, it can be possible to
conclude that choice of shop layout design is basic
both for system e$ciency and agility (Fig. 9). In
particular, these two macro performances show an
adverse trend, because of the trade-o! between
set-up saving and routing #exibility. Essentially, it
54
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Table 10
Test-t for mean values di!erence of observed performance groups
Level comparison Shop layout
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
E$ciency
Agility
Mix balancing
I-a variability
0}1
0}2
1}2
0}1
0}2
1}2
0}1
0}2
1}2
0.093
0.034
0.034
0.010
0.053
0.171
0.000
0.000
0.006
0.000
0.021
0.804
0.074
0.074
0.004
0.073
0.987
0.000
0.000
0.000
0.000
0.055
0.170
0.454
0.469
0.041
0.690
0.047
0.000
0.000
0.001
0.000
0.614
0.734
0.618
0.627
0.602
0.301
0.211
0.982
0.196
0.096
0.443
0.684
0.555
0.401
0.401
0.578
0.234
0.699
0.395
0.105
0.014
0.152
0.122
0.746
0.645
0.640
0.917
0.729
0.023
0.422
0.586
0.208
0.553
0.164
0.987
0.227
0.225
0.535
0.336
0.826
0.997
0.995
1.000
0.993
0.242
0.047
0.002
0.002
0.042
0.006
0.265
0.948
0.993
1.000
0.976
0.025
0.067
0.005
0.004
0.072
0.014
0.372
0.945
0.998
1.000
0.983
0.030
Fig. 5. Average work in process against inter-arrival time CV (balanced mix).
is evident that a shop with dedicated lines has the
fastest queue dynamics, making easier the execution of dispatched orders for parts production. Line
dedication makes possible to avoid system overload so that it maintains a low steady level of agility
over time. On the contrary, job shop proves to be
more e$cient, since production #ows are designed
and managed stressing the importance of load balancing among system work centres. This condition
imposes the rationalisation of system resources.
Consequently, it implies a better work centres'
average utilisation, a reduction in bottlenecks' criticality and a lower number of servers needed
to execute orders assigned for production of
dispatched parts.
With reference to research questions 2 and 3,
results analysis shows that the two components of
demand variability have di!erent impacts on system performances. In particular, the same considerations just exposed for job shop layout seem to be
well-founded for the mix balancing factor. Mix
unbalancing reduces system e$ciency because of
the higher number of servers needed. At the same
time, given the set-up saving implied, it raises system agility (Fig. 10). Unfortunately statistical inference does not allow to consider these results as
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Fig. 6. Average lead time against inter-arrival time CV (strongly unbalanced mix).
Fig. 7. Average system utilisation against shop layout choice (balanced mix).
Fig. 8. Server needed against mix balancing.
55
56
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Table 11
Mean values for e$ciency and agility indexes
E$ciency
Agility
Layout
Job
FCS
Flow
32.7818
18.4445
6.9569
1.4459
11.3107
14.8392
I-a variability
0%
40%
100%
19.4673
19.4190
19.2969
17.2102
9.2331
1.1525
Mix balancing
High
Medium
Low
23.3302
19.0858
15.7671
5.0043
6.1863
16.4052
Fig. 11. Macro performances against lots inter-arrival time
variability.
driven on work centres bu!ers "lling dynamics.
While lots inter-arrival time increases, the #uctuation range of queues content increases heavily and
irregularly over time. Such an e!ect implies a significant worsening of system agility mean value, as
plotted in Fig. 11. So, aiming to answer research
questions 2 and 3, it can be stated that demand
variability has a strong e!ect only on system agility
and not on e$ciency performances. Besides, this
e!ect is due to lots inter-arrival time variability and
not to mix balancing.
6. Conclusions
Fig. 9. Macro performances against shop layout design.
Fig. 10. Macro performances against mix balancing.
both signi"cant and generalisable, so that it is not
possible to state anything about the impact of such
factors on system performances. With regard to lots
inter-arrival time variability, data in Table 11 show
that system e$ciency does not depend in any way
on this factor. On the contrary, the impact on
system agility is very heavy because of the e!ect
In this paper three alternative manufacturing
layouts (job shop, #exible cell shop, #ow shop) are
compared aiming to test their robustness with
respect to system input variability, modelled
through the variation coe$cient of lots' inter-arrival time to the system and unbalancing of product
mix. Results obtained by means of simulation experiments show that trade-o! between set-up saving and routing #exibility has a well evident impact
on system macro performances: agility and e$ciency. Set-up saving allows to reduce in-queue waiting
time, routing #exibility allows to minimise process
resources assigned to the manufacture system. So
alternative layout con"gurations have opposite behaviour. Job shop process layout involves a system
resource organisation which provides best e$ciency performances (average utilisation, utilisation
balancing, number of servers needed,2), since the
design criterion imposes absolute priority to resources minimisation allowing to have maximum
routing #exibility. In #ow shop layout, dedicated
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
nature of resources allows maximum set-up saving
but, at the same time, provides routing in#exibility.
So having the best queue related performances
(work in process, average bu!er size, average bu!er
time, lead time,2) it is necessary to revert to e$ciency. Obviously, hybrid forms should provide
intermediate performance. Flexible cell shop layout
ensures an intermediate level of set-up saving and
an intermediate level of routing #exibility, i.e. an
intermediate level of e$ciency and agility.
Considering di!erent demand variability conditions, in terms of inter-arrival time variation and
mix balancing, the experimental analysis proved
that the larger e!ect on system performances is due
to the "rst factor, not to the second one. However,
this e!ect is limited to agility performances (queue
and time related performances). System e$ciency
seems not to depend on demand variability.
Such results indicate that manufacturing layout
choice depends on the particular performance considered critical by the decision maker. There does
not exist a layout that overcomes others in an
absolute way. Each one is characterised by a proper
level of routing #exibility and set-up saving.
With respect to literature indications, this paper
returns consistency to the hypotheses of cellular
manufacturing layout choice, which guarantees intermediate level of system e$ciency and agility.
Empirical evidence shows that layout alternative
to job shop or, in general, functional forms, almost
always brought a signi"cant improvement in overall system performance. The need to solve this
contradiction between theoretical models and empirical research, points out that in comparative
theoretical studies (like the presented one), some
critical factors are not considered, such as labour,
labour}machine interaction, run time (and not only
set-up) reduction, inter-cell movement, not instantaneous movement, and so on.
So, this study would not mind a methodological
contribution to the choice of the best manufacturing layout, due to the data-dependent nature of
system performance, but it proposes some clarifying elements for the decision making process which
companies apply when they decide to implement or
convert their manufacturing layout.
Results obtained in this research have to be
checked for a more complex manufacturing system.
57
Authors are performing a sensitivity analysis to test
signi"cance of part-machine matrix size on presented research questions. It would be interesting also
to remove the hypotheses of zero move time and
quantify the e!ect of other demand related variables, such priority assignment for families scheduling, rules for bu!er scheduling and so on.
References
[1] J.S. Noble, J.M.A. Tanchoco, Design justi"cation of manufacturing systems, The International Journal of Flexible
Manufacturing System 5 (1993) 5}25.
[2] J.S. Morris, R.J. Tersine, A simulation analysis of factors
in#uencing the attractiveness of group technology cellular
layouts, Management Science 36 (12) (1990) 1567}1578.
[3] B.J. Jensen, M.K. Malhotra, P.R. Philipoom, Machine
dedication and process #exibility in a group technology
environment, Journal of Operations Management 14
(1996) 19}39.
[4] N.C. Suresh, Partitioning work centers for group technology: Analytical extension and shop-level simulation
investigation, Decision Sciences 23 (2) (1992) 267}290.
[5] N.C. Suresh, Partitioning work centers for group technology: Insights from an analytical model, Decision Sciences
22 (1991) 772}791.
[6] N.C. Suresh, J.R. Meredith, Coping with the loss of pooling synergy in cellular manufacturing systems, Management Sciences 40 (4) (1994) 466}483.
[7] B.B. Flynn, F.R. Jacobs, An experimental comparison of
cellular (group technology) layout with process layout,
Decision Sciences 18 (1987) 562}581.
[8] C.L. Ang, P.C. Willey, A comparative study of the performance of pure and hybrid group technology manufacturing systems using computer simulation techniques, The
International Journal of Production Research 22 (1984)
193}233.
[9] R. Leonard, K. Rathmill, The group technology myths,
Management Today January (1977) 66}69.
[10] C.A. D'Angelo, M. Gastaldi, N. Levialdi, Dynamic analysis of a #exible manufacturing system: A real case application, Computer Integrated Manufacturing Systems 9 (2)
(1995) 101}110.
[11] C.A. D'Angelo, M. Gastaldi, N. Levialdi, Performance
analysis of a #exible manufacturing system: A statistical
approach, International Journal of Production Economics
56/57 (1998) 47}59.
[12] R.G. Askin, C.R. Standridge, Modeling and Analysis of
Manufacturing Systems, Wiley, New York, 1993.
[13] G.S. Fishman, Estimating sample size in computer simulation experiments, Management Science 18 (1971) 21}38.
[14] A. D'Angelo, M. Gastaldi, N. Levialdi, Optimizing #ows
and resources in a #exible production system, IASI-CNR
Internal Report, No. 436, Italy, 1996.
Production variability and shop con"guration: An
experimental analysis
Andrea D'Angelo!,*, Massimo Gastaldi", Nathan Levialdi#
!Department of Production, Systems and Computer Science, University of Rome-Tor Vergata, Italy
"Department of Electrical Engineering, University of L'Aquila, Italy
#Department of Industrial Engineering, University of Perugia, Italy
Received 6 June 1998; accepted 4 February 1999
Abstract
The selection of an e$cient shop layout at manufacturing level is a strategic problem, involving considerable
immobilisation of "nancial resources. Often the problem is hierarchically solved on several levels. In the present paper we
focus on the level of physical system planning and try to de"ne the best shop con"guration in terms of process resources
layout (work centres organisation), considering di!erent variability conditions for demand (system input). Data obtained
from simulation experiments are being statistically analysed to clear the weak and strong points for each con"guration
(job shop, #exible cell shop, #ow shop). ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: Manufacturing systems; Shop layout; Simulation; Performance evaluation; Production variability
1. Introduction
The selection of an e$cient productive layout at
manufacturing level is a strategic problem, involving considerable immobilisation of "nancial
resources. Therefore, planning and design of production lines include a careful analysis [1], devoted
to individuate technological bind that the related
production imposes, factors in#uencing strategic
system performances and the opportune tool for
the selection of the best solution. Often the problem
is hierarchically resolved on several levels. In particular, the level of physical system planning requires a further distinction of physical components
* Corresponding author. Tel./Fax: #39-6-725-97358.
E-mail address: dangelo@disp.uniroma2.it (A. D'Angelo).
from the control logic. In the present paper we
focus on the "rst item: organisation of process
resources layout. The manufacturing layout choice
passes in fact through the quanti"cation of tradeo! between routing #exibility and set-up savings,
i.e., performances of alternative con"gurations
showing symmetric advantages and disadvantages.
In particular, the analysis goes over a restricted set
of alternative choices: job shop, cellular manufacturing, #ow shop and hybrid systems. The "rst type
is a process layout and involves a system resource
organisation in isolated work centres, in which
machines perform all the same type of processing
on parts to be produced. The second type of layout
is organised in isolated cells in which only a suitably scheduled product family is processed. That is,
system resources are allocated at di!erent working
departments on the basis of technological binds
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 0 1 2 - 2
44
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
imposed by production cycle of products families.
The third type of layout provides the organisation
of dedicated production #ow lines in which part
types follow obliged and unidirectional routes. Besides there exist other hybrid forms which mediate
functional features of the two types just described.
The attractiveness of each type of con"guration is
basically tied to the particular system performance
considered by management. Aim of this research is
to compare alternative productive layouts testing
their robustness with respect to system input variability. That is, we want to quantify trade-o!
between routing #exibility and set-up savings with
respect to di!erent condition of demand variability.
Such variability will be modelled through the variation coe$cient of lots inter-arrival time to the
system and through unbalancing of product mix.
Simulation experiments will be conducted with this
aim: gathering the correlation between system performances and such experimental factors testing
the relative statistical signi"cance.
The paper is organised as follows: in Section 2,
literature contributions are reviewed aiming to
underline the main topics investigated in this
research area. In Section 3, the system under study
is brie#y described, while in Section 4, the paper's
aims are presented in terms of research questions,
experimen-independent factors and the observed
dependent variable. Data obtained by simulation
experiments are analysed in Section 5 and "nally
Section 6 concludes the paper discussing the major
results and possible future research issues.
2. Relevant literature review
In literature, works that investigate dynamics
related to productive #ows in a manufacturing system agree in recognising the process layout to be
superior in terms of e$ciency performance. Morris
and Tersine [2] proved that the job shop has the
best performance in almost all experimental test
conditions. Observed system performances (mean
throughput time and the work in process inventory) are analysed with respect to a series of test
factors such as demand stability, productive #ow
direction and set-up time. Statistical analysis conducted on experimental results data shows that
shop layout organised in manufacturing cells (each
dedicated to a single part family) provides clear
advantages only for a high level of set-up time
reductions. In reality, the choice of Group Technology implementation is often linked to advantages
related to an improvement in quality and rework
costs, since workers are held accountable for a completed product rather than a completed process.
Unfortunately these results are dependent upon
human response and it is very di$cult to model
them in an analytical way. Jensen et al. [3] get the
same results considering the impact of di!erent
experimental factors such as bu!er scheduling rules
and machine customisation. In general, conclusions
of in-depth researches show that shop layout organised in manufacturing cells (which imposes the
arrangement of dedicated routes for objects processed) shows an extreme in#exibility. Suresh [4,5]
and Suresh and Meredith [6] utilised queuing
models to analyse e!ects of partitioning work
centres in a shop level investigation. Even the introduction of set-up reductions partitioning does not
show substantial bene"ts. The obtained results are
con"rmed utilising a simulation pattern which
allows to model a wider number of factors and
thereby ensure a greater reality adhesion. Once
more the attractiveness of cellular layout would
seem strongly tied to particular combinations of lot
size and set-up reduction levels. Besides, it demonstrates that a part of the functional in#exibility of
such a shop can be avoided allowing inter-cell
movements. The study of Flynn and Jacobs [7]
compares di!erent strategy for arranging machines
in a facility. Computer simulation was used to
compare process layout (the arrangement of groups
of machines where the machine within a group are
interchangeable) to cellular layout designed using
group technology concepts. Four layout strategies,
including process layout, cellular layout, and two
hybrid layouts, were compared in two machineshop models. Despite the shop using cellular
layouts had shorter setup times and shorter distances traveled, the shop with process layout had
better performance. This suggests that a wellorganized traditional job shop may be able to
achieve overall performance that at least is comparable to that of the same shop using cellular layout.
A growing amount of literature is then critical of
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
group technology and cellular manufacturing. Ang
and Willey [8] stated that group technology and
hence cellular manufacturing lacks the #exibility
necessary for coping with work-load variations.
They concluded that this choice is not always economically justi"ed in practice. Also Leonard and
Rathmill [9], although formerly proponents of
group technology, believed good process layout
would prove superior in comparison with cellular
layout shop con"guration.
In generating the part-machine matrix we do not
impose the condition of unidirectional #ow, i.e., it is
possible for a part type to be processed by a machine visited before. Such an hypothesis was considered important for a more realistic adhesion. It
introduces a complication which is partly balanced
by the extreme easiness of the part-machine matrix.
Results of the random formulation are reported in
Table 2. For each family the manufacturing cycle
sequencing is shown in terms of visited machines
(the mean value of cycle time for each operation).
As you can see, part family P undergoes six opera2
tions, while P only three. Moreover, only manu4
facturing cycle sequencing for P does not involve
4
backward processing, since its cycle provides
operations on all di!erent machines. To this purpose parts belonging to product family P have
2
a double reprocessing: its cycle involves passage on
machines 4 and 6, in two di!erent situations.
Each work centre is supplied with an input bu!er
of unlimited capacity. Parts arriving in a department to be processed are stored in such a bu!er and
are scheduled through a simple "rst-come-"rst-served rule. Each machine can process only one operation at a time, and is continuously available for
production when it is not processing a part. Two
set-ups may occur when a part is dispatched to
a machine. The "rst one represents the requested
intervention before a part was processed (10% of
mean processing time). The second one represents
the requested intervention on machine which has to
process a part belonging to a di!erent family with
respect to the last one dispatched (50% of mean
processing time). Processing times relative to machine operations are stochastically distributed in a
log-normal curve around the mean value shown in
Table 2 (variation coe$cient of such a distribution
3. System description
In the above contributions, production system
was assigned through a more or less complex partmachine matrix. Here this matrix was randomly
generated (Table 1).
To this purpose, the following assumptions were
made:
f total number of possible processing (6);
f total number of product families (4);
f minimum and maximum number of operations
expected for each product family (3}6);
f minimum and maximum value of cycle time relative to each processing for each kind of part type
(5}10 minutes).
Table 1
Part-machine matrix
P
1
P
2
P
3
P
4
WC
1
WC
2
WC
3
x
x
x
x
x
x
x
x
x
WC
4
WC
5
WC
6
x
x
x
45
x
x
x
Table 2
Family sequencing and mean processing times on each machine
Process P 1
Part family B
P
1
P
2
P
3
P
4
WC
2
WC
6
WC
4
WC
2
2
(7)
(7)
(10)
(6)
WC
5
WC
4
WC
6
WC
5
3
(5)
(9)
(6)
(6)
WC
1
WC
1
WC
4
WC
1
(7)
(8)
(10)
(6)
4
5
6
WC (9)
5
WC (7)
3
WC (8)
2
WC (10)
3
WC (8)
4
WC (8)
6
WC (6)
2
46
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
is 0.2). Moreover it is assumed that all machines are
subject to breakdown events. For each server allocated in a work centre, these events have a probability occurrence distributed in a log-normal curve
around a mean value of 1000 minutes and a standard deviation of 100 minutes. Mean time to repair,
instead, is distributed in a Gamma curve with
a mean value of 50 minutes. Parts to be produced
arrive at the system in lots of "xed size (5). In
designing work centres, cells or #ow lines, in terms
of minimum server allocation necessary to respect
the continuity bound and to avoid system parts'
accumulation, we make use of the following model
[10,11]:
4
P
n " 505 + C ¹p
j
ij
i 60;
m j/1
∀i"1,2, 6,
(1)
where P is the hourly total system input, ; is the
505
m
average expected utilisation, C is the percentage
j
mix relative to family j ( j"1,2, 4) and ¹p is the
ij
average processing time for parts belonging to family j, for operation i.
Total system load is balanced by the production
capacity of each centre, cell or line, given a certain
expected average system utilisation (; ). This varim
able quanti"es the supposed servers' availability,
which is limited by set-up and breakdown events.
The use of such a formula allows balancing system
production resources. If we suppose an average
expected utilisation ; , the number n given in Eq.
m
i
(1) represents the minimum number of servers in
work centre, cell or line i, complying with the
steady continuity bound (System Input"System
Output [12]). In its turn, the observance of such
a bound allows to examine (after an unavoidable
transient warm-up period) system variables' steady
state.
4. Experimental design
The problem of the shop manufacturing layout
design is often solved by means of simulation models which enable to test the e!ect of variability of
a few experimental factors (independent variables)
on typical system performances (dependent variables). This paper focuses on the quanti"cation of
trade-o! between routing #exibility and set-up savings, detecting the robustness of di!erent shop
layouts with respect to demand variability. Three
overall research questions are speci"cally addressed:
f Research question 1: Do di!erences in shop
layout organisation have a meaningful e!ect on
system performances?
f Research question 2: Does demand variability
have an impact on system performances?
This second point has been addressed in order to
be able to generalise results of the study to a wider
variety of shops characterised by di!erent demand
conditions.
f Research question 3: Which component of demand variability has the largest e!ect on system
performances?
The analysis will detect the robustness of shop
layouts with respect to demand variability
modelled in terms of lots inter-arrival time variability (I-a variability) and mix balancing. Hence, we
consider three independent variables (see Table 3).
Each one has three distinct levels for a total of 27
possible combinations which de"ne as much con"gurations.
Shop layout: Job shop is a process layout and
involves a system resource organisation in isolated
work centres in which all machines perform the
same type of processing on parts to be produced
(Fig. 1).
Flexible cell shop involves a layout organised in
isolated cells in which only a suitably scheduled
product family is processed (Fig. 2). That is, system
resources are allocated at di!erent working departments on the basis of technological binds imposed
by the production cycle of products families.
Table 3
Independent variables for the experimental analysis
Level Shop layout
0
1
2
Mix
I-a variability
Job shop
Balanced
Null
Flexible cell shop Medium unbalanced Medium
Flow shop
Strongly unbalanced Large
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Demand variability: Demand variability has two
main components:
f Variability of inter-arrival time of lots to the
system;
f Balancing of product mix.
The "rst factor is modelled by means of the
variation coe$cient of a log-normal distribution
ranging from 0%, 40% and 100% of mean value.
The second one is modelled by means of mix ratios
selected so that system input is divided up among
various families in accordance with a "xed balanc-
47
ing level (Fig. 3). In particular, the balancing is
a function of P load/P load ratio which ranges
1
4
from 1 to 4 to 10 (Table 4).
Considering a total system load of 20 parts per
hour and a "xed lot size of 5 parts each, the scheduling scheme is as shown in Table 5.
4.1. Simulation models
The three research questions are investigated
with reference to the manufacturing system described in Section 3. Developing a full factorial
experimental design, we consider 27 models de"ned
by all possible combinations of experimental factor
levels. These models were coded in a special software tool (WITNESS 7.0t). In de"ning simulation
experiments three issues have been considered:
f the e!ect of bias deriving from starting conditions, resulting in atypical system behaviour;
f observation time length of system events for each
simulation run;
f number of replications for each run (the experiment sample size).
Fig. 1. Job shop layout.
The "rst issue was detected by means of pilot
runs. System variables' values tracking allows to
analyse transient behaviour and to determine at
which point system status reaches steady state. To
this purpose we monitored the trend of mean #ow
Fig. 2. Flexible cell shop layout.
48
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Fig. 3. Flow shop layout.
Table 4
Demand variability, levels of mix balancing
Table 5
Scheduling scheme, mean inter-arrival time of lots to the system
Part family
Part family
P
1
P
2
P
3
P
4
Mix ratio
Product mix
Balanced
Med.
unbalanced
Strongly
unbalanced
25%
25%
25%
25%
1:1
40%
30%
20%
10%
4:1
50%
30%
15%
5%
10:1
time percentage variation as signal for detecting
bias e!ect deriving from starting conditions (use of
alternative variables such as work in process, average bu!er size etc. lead to the same results).
Fig. 4 plots the trend of this variable over the "rst
30.000 running time units (minutes). As evident in
this particular situation, plot #uctuations tend to
fade away in about 210 hours. Clearly, at that
moment, mean #ow time for manufactured parts
becomes stable around its steady value.
Other plots show the same results for other
experimental conditions, even if inter-arrival variation provides persistent and larger #uctuations
around the steady value, which is zero. In all cases,
we can assume that transient state "nishes after
P
1
P
2
P
3
P
4
Mix ratio
Product mix
Balanced
Med.
unbalanced
Strongly
unbalanced
60 min
60 min
60 min
60 min
1:1
37.5 min
75 min
50 min
150 min
4:1
30 min
50 min
100 min
300 min
10:1
250 hours (15.000 running minutes) and data collection can start after this time length. Hence, in each
simulation run, data relative to "rst 15.000 minutes
are cleared to avoid e!ects of initialisation bias.
Once the point at which system reaches the steady
state has been selected, The second issue involves
determination of total replication time length
and sample size. For this purpose, we considered
"ve replications for each experiment. This sample
size was chosen to provide not-correlated mean
values for observed events [13]. At the same time
we "xed 90.000 minutes of total running for each
experiment replication. This choice assures a duration of 1250 hours (about 30 weeks) of system
steady functioning.
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
49
Fig. 4. Lead time percentage variation over time for di!erent models.
4.2. Performance measures: Dependent observed
variables
most interesting manufacturing system performances and are abbreviated named as follows:
The dependent variables observed in steady state
system simulation running have been selected according to literature indications [14] relatively to
f LT: average lead time
f WIP: average work in process
f Buf. size: average bu!er size
50
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
f Buf. time: average time spent in bu!ers
f CV buf.: coe$cient of variation of bu!er time
spent in bu!ers
f Av. Util.: average work centres' utilisation
f CV Util.: coe$cient of variation of work centres
utilisation
Besides, we add two other variables:
f Server: minimum number of servers in system
f Volume: total system output
The "rst one is determined according to Eq. (1).
It represents the total number of servers located to
each work centre, i.e. the minimum resource necessary to respect the continuity bound and avoid
system parts' accumulation, given an average
expected utilisation. Hence, we consider this variable as a measure of performance, since its value
re#ects the ability of the system to outperform the
pre-assigned scheduled production, as a proper e$ciency index. The second one quanti"es the number
of parts shipped out from the system. Since we have
imposed a resource sizing in order to avoid parts
accumulation and observe steady state running,
such a variable is not considered as a proper performance, because at any instant the steady continuity
bound (System Input"System Output) is given. Such
a condition imposes that total production volume
will be always the same in each experimental condition, not dependent on demand variability level or
shop layout design (P , hourly total system input,
505
was imposed equal to 20 parts). However, in this
context, we consider this variable, since it will be
very interesting to verify the veracity of considerations just exposed.
4.3. Other model assumptions
Utilised simulation models do not contemplate
time consuming computation for material handling
or inter-machine movements. Given the model
assumptions, we suppose, in fact, that time shift due
to parts movement does not impact system performances in a signi"cant manner. In reality the
weight of move time on system performances (in
particular on #ow time and queue related variables)
can be very di!erent for di!erent shop layout or-
ganisations. In other words, part of the di!erence
observed in lead time performance between job
shop and #ow shop, can be explained in terms of
move time of parts along manufacturing lines. But
such an e!ect is not numerically relevant with
respect to others, and in particular for those interested in the aim of our research. Hence, we assume
zero move time. Relevant components of time
dependent variables are those due to set-up, machine processes and in queue waiting.
5. Results analysis
Experimental results of 27 simulation models,
conducted to quantify the e!ect of independent
variables on system performances, are shown in
Table 6.
To answer research questions presented in Section 4, "rstly we proceed analysing results aiming
to discover possible correlation between observed
independent variables. So the correlation matrix
for nine system performances was built in order to
check the presence of a possible common trend.
Such an analysis is shown in Table 7.
Choice of such a functional expression for e$ciency index is justi"ed considering the positive
e!ect of average system utilisation (Av. Util.) and
the negative one of utilisation variation coe$cient
(CV Util.) and total number of system servers (Server) on global performance. As expressed above, in
fact, a manufacturing system is considered e$cient
if the resource needed to process a given production
volume is minimal (minimum Server), and if system
work-load is well balanced between work centres
(minimum CV Util, maximum Av. Util.). With reference to the agility index it has been considered
the negative e!ect of queue related performances
(WIP, Buf. size, Buf. time), and of lead time.
A manufacturing system is considered agile or lean
if bu!ers content relains limited on time. This condition avoids system congestion, reduces production locking up and parts #ow times. Besides, in
both cases, expressions involve constants for
numerical normalisation of observed values.
Coe$cients in the matrix highlight a strong correlation between two distinct clusters of variables
(pointed out in bold). Once identi"ed, such clusters
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
51
Table 6
Mean values for observed dependent variables for each experimental model
Run
Independent variables
Dependent variables
Layout
X
1
Mix bal. I-a. Var. Volume
X
X
Y
2
3
1
WIP
Y
2
LT
Y
3
Buf. size Buf. time CV buf.
Y
Y
Y
4
5
6
Av. Util. CV Util. Server
Y
Y
Y
7
8
9
1
2
3
4
5
6
7
8
9
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.981
25.130
24.988
24.994
25.030
24.886
25.001
24.873
25.086
49.85
61.31
110.98
41.69
46.29
86.41
40.63
43.47
102.79
149.23
182.28
329.73
124.84
138.34
259.42
121.66
130.87
304.69
5.48
7.40
15.73
3.90
4.68
11.45
3.72
4.23
14.15
27.51
37.10
78.44
14.65
17.52
42.07
14.12
16.46
55.97
147.89
155.02
150.82
70.37
68.25
56.23
85.95
90.36
111.25
60.14
60.51
60.05
62.14
62.22
61.94
61.59
61.26
61.82
8.53
8.69
8.30
8.63
8.62
8.74
9.68
9.64
9.84
20
20
20
21
21
21
22
22
22
10
11
12
13
14
15
16
17
18
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.999
24.956
24.798
25.010
24.955
24.816
24.997
24.955
24.867
32.63
35.03
57.98
32.03
36.33
57.99
29.47
32.78
48.97
97.74
105.10
174.39
95.88
109.02
174.52
88.28
98.41
147.28
1.84
2.12
4.72
1.66
2.15
4.60
1.37
1.76
3.58
12.02
13.48
27.87
10.78
13.54
28.80
8.64
11.25
22.95
72.32
65.52
82.84
74.64
76.93
89.13
87.56
97.21
108.63
56.54
56.52
56.16
57.07
56.93
56.57
55.96
55.85
55.69
11.35
11.22
11.98
14.34
14.29
14.42
15.82
15.90
15.55
21
21
21
23
23
23
24
24
24
19
20
21
22
23
24
25
26
27
2
2
2
2
2
2
2
2
2
0
0
0
1
1
1
2
2
2
0
1
2
0
1
2
0
1
2
24.987
24.995
25.021
25.015
25.048
24.983
25.014
25.061
24.754
38.35
42.83
64.45
38.05
43.21
66.08
29.80
33.24
46.60
114.90
128.18
192.17
113.85
129.10
197.33
89.19
99.25
140.86
1.27
1.51
2.65
1.19
1.46
2.67
0.74
0.92
1.63
15.26
18.02
31.71
13.64
16.30
29.48
9.08
10.90
18.32
74.02
79.70
87.06
91.26
96.78
124.54
116.60
116.22
152.27
51.74
51.80
51.69
49.57
49.66
49.49
43.12
43.22
42.72
19.04
19.44
19.10
32.31
32.40
32.41
39.70
39.52
39.50
24
24
24
26
26
26
29
29
29
Table 7
Correlation matrix for observed dependent variables
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
Volume
WIP
LT
Buf. Size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
1.000
0.002
1.000
!0.009
1.000
1.000
0.066
0.922
0.922
1.000
0.042
0.974
0.973
0.925
1.000
0.070
0.328
0.326
0.271
0.424
1.000
0.078
0.357
0.357
0.582
0.360
!0.59
1.000
!0.010
!0.307
!0.308
!0.519
!0.339
0.294
2 0.958
1.000
!0.063
!0.366
!0.366
!0.556
!0.412
0.213
2 0.927
0.961
1.000
52
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
are de"ned in a synthetic way through an e$ciency
and agility index. Their numerical expression is
underlined in Table 8.
The statistical regression analysis allows to check
the signi"cance of experimental factors with reference to their in#uence on system performances,
considering the hypotheses of a linear dependence.
In Table 9 values of multiple correlation coe$cient
R2 (columns 2}4) are listed for rating of "rst order
e!ect for each di!erent shop layout con"guration.
In column 5, R2 value is presented for all the e!ects,
up to third order, while in column 6 the ratio is
relative to the percentage due to "rst order terms.
Variance explained by experimental factors at "rst
order level is rather high in cases highlighted in
bold. In job shop and #ow shop cases volume seems
not to be a!ected by experimental factors' variations. Also the ANOVA test (F-value in column 7)
strengthens such results. This observation is e!ective for average utilisation too, for two of the three
shop layouts.
The analysis clearly indicates that the impact of
Inter-arrival time variation mainly regards the agility performances (i.e. lead time, work in process,
average bu!er size, average time spent in bu!er),
while the impact of mix balancing variation mainly
regards the e$ciency performances (i.e. number of
servers in the system and variation coe$cient of
resource utilisation).
Through a Student T-test, statistical results analysis allowed to highlight the presence of meaningful
di!erences in performance mean values, observed
Table 8
Components and numerical expressions for e$ciency and agility
indexes
Components Expressions
Cluster 1: Ezciency z
z
Cluster 2: Agility
Av. Util.
CV Util.
z
Server
z
z
WIP
LT
z
z
Buf. size
Buf. time
100 ) Av.Util.
CVUtil. ) Server
106
WIP ) LT ) Buf.size ) Buf.time
in groups corresponding to the experimental factor
level combinations. Preliminary F-test was
performed to test the homogeneity of means for
di!erent groups examined, by means of sample
data analysis. In Table 10 Student T-test data are
listed. Almost all di!erences observed by varying
shop layout level are signi"cant at an a level of
about 0.05 (bold values). We cannot have the same
statement for mix balancing factor, while relatively
to inter-arrival time variation we observe a signi"cant di!erence in system behaviour (particularly for
queue related performances), for groups of 0 and
2 levels. That is, only for a remarkable increase in
inter-arrival time variability (variation coe$cient
ranging from 0% to 100%), a real worsening of
system agility can be stated.
In curves shown in Figs. 5}8 the e!ects whose
statistics are signi"cant, are plotted with reference
to di!erent experimental conditions.
Queue related performances deteriorate against
inter-arrival time variability increase (Fig. 5). Job
shop layout shows the worst levels, while #exible
cell shop the best ones. The plotted trend is the
same for the lead time variable (Fig. 6), but in this
case the unbalancing #attens curves of #exible cell
shop and #ow shop layouts.
Fig. 7 shows that, apart from system input
variability, the choice of a shop layout with dedicated resources involves a decrease in system e$ciency, in terms of average work centres utilisation.
Besides, the curve has a rather similar trend for
di!erent mix balancing conditions (plots not presented here). The only experimental factor having
a strong e!ect on such a variable is hence the shop
layout design.
Instead, in terms of mix balancing variability, the
only e!ect supported by statistical indexes is relative to the dependent variable named server. Mix
unbalancing implies an increase of resource needed
in work centres (Fig. 8). In fact it involves an increase in the presence of parts characterised by
longer sequences and longer processing times.
Hence, the most evident e!ect is the increase of
system load, with consequent need for additional
servers, as established in Eq. (1).
Finally, let us consider the data shown in
Table 11. Relative to aggregate indexes de"ned
above as e$ciency and agility, mean values are
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
53
Table 9
Multiple regression coe$cient (R2) and test-F for each observed variable
Mix. bal.
I-a. var.
I order
Tot.
% I order
(a) Job shop
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
0.0584
0.0334
0.0333
0.0401
0.1356
0.3843
0.4484
0.8116
1.0000
0.0009
0.7595
0.7630
0.7530
0.6139
0.0028
0.0001
0.0001
0.0000
0.0593
0.7929
0.7963
0.7931
0.7495
0.3870
0.4485
0.8117
1.0000
0.1618
0.9776
0.9803
0.9787
0.9642
0.9710
0.9505
0.9756
1.0000
0.3666
0.8111
0.8123
0.8103
0.7773
0.3986
0.4719
0.8320
1.0000
0.1892!
11.4860
11.729
11.4983
8.9745
1.8942!
2.4398!
12.9283
d
E$ciency
Agility
0.8732
0.2452
0.0000
0.5778
0.8732
0.8230
0.9894
0.9749
0.8825
0.8442
20.6522
13.9473
(b) Flexible cell shop
Volume
0.0140
WIP
0.0332
LT
0.0327
Buf. size
0.0475
Buf. time
0.0384
CV buf.
0.6134
Av. Util.
0.2635
CV Util.
0.9498
Server
0.9643
0.8853
0.7995
0.8010
0.7941
0.8024
0.2463
0.1202
0.0011
0.0000
0.8993
0.8327
0.8336
0.8416
0.8407
0.8597
0.3837
0.9509
0.9643
0.9873
0.9913
0.9912
0.9915
0.9909
0.9699
0.9909
0.9948
1.0000
0.9109
0.8401
0.8411
0.8488
0.8484
0.8864
0.3872
0.9558
0.9643
26.7810
14.9329
15.0342
15.9341
15.8362
18.3870
1.8677!
58.1273
81.0000
E$ciency
Agility
0.9296
0.1263
0.0030
0.7480
0.9326
0.8743
0.9957
0.9863
0.9366
0.8865
41.5118
20.8710
(c) Flow shop
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
0.0771
0.1667
0.1628
0.2040
0.2478
0.6873
0.9211
0.9720
0.9868
0.1669
0.6478
0.6548
0.6263
0.6000
0.2218
0.0004
0.0000
0.0000
0.2440
0.8146
0.8177
0.8303
0.8478
0.9092
0.9215
0.9720
0.9868
0.7855
0.9840
0.9855
0.9850
0.9876
0.9746
0.9998
0.9999
1.0000
0.3106
0.8278
0.8297
0.8429
0.8584
0.9329
0.9216
0.9721
0.9868
0.9680!
13.1791
13.4544
14.6768
16.7052
30.0310
35.1925
104.0524
225.0000
E$ciency
Agility
0.9422
0.3400
0.0000
0.3673
0.9422
0.7073
0.9998
0.9594
0.9425
0.7372
48.9354
7.2481
F-value
! Not signi"cant.
shown for di!erent combinations of experimental
factors.
Histograms built on such data are plotted in
Figs. 9}11. With reference to research question
1 expressed in Section 4, it can be possible to
conclude that choice of shop layout design is basic
both for system e$ciency and agility (Fig. 9). In
particular, these two macro performances show an
adverse trend, because of the trade-o! between
set-up saving and routing #exibility. Essentially, it
54
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Table 10
Test-t for mean values di!erence of observed performance groups
Level comparison Shop layout
Volume
WIP
LT
Buf. size
Buf. time
CV buf.
Av. Util.
CV Util.
Server
E$ciency
Agility
Mix balancing
I-a variability
0}1
0}2
1}2
0}1
0}2
1}2
0}1
0}2
1}2
0.093
0.034
0.034
0.010
0.053
0.171
0.000
0.000
0.006
0.000
0.021
0.804
0.074
0.074
0.004
0.073
0.987
0.000
0.000
0.000
0.000
0.055
0.170
0.454
0.469
0.041
0.690
0.047
0.000
0.000
0.001
0.000
0.614
0.734
0.618
0.627
0.602
0.301
0.211
0.982
0.196
0.096
0.443
0.684
0.555
0.401
0.401
0.578
0.234
0.699
0.395
0.105
0.014
0.152
0.122
0.746
0.645
0.640
0.917
0.729
0.023
0.422
0.586
0.208
0.553
0.164
0.987
0.227
0.225
0.535
0.336
0.826
0.997
0.995
1.000
0.993
0.242
0.047
0.002
0.002
0.042
0.006
0.265
0.948
0.993
1.000
0.976
0.025
0.067
0.005
0.004
0.072
0.014
0.372
0.945
0.998
1.000
0.983
0.030
Fig. 5. Average work in process against inter-arrival time CV (balanced mix).
is evident that a shop with dedicated lines has the
fastest queue dynamics, making easier the execution of dispatched orders for parts production. Line
dedication makes possible to avoid system overload so that it maintains a low steady level of agility
over time. On the contrary, job shop proves to be
more e$cient, since production #ows are designed
and managed stressing the importance of load balancing among system work centres. This condition
imposes the rationalisation of system resources.
Consequently, it implies a better work centres'
average utilisation, a reduction in bottlenecks' criticality and a lower number of servers needed
to execute orders assigned for production of
dispatched parts.
With reference to research questions 2 and 3,
results analysis shows that the two components of
demand variability have di!erent impacts on system performances. In particular, the same considerations just exposed for job shop layout seem to be
well-founded for the mix balancing factor. Mix
unbalancing reduces system e$ciency because of
the higher number of servers needed. At the same
time, given the set-up saving implied, it raises system agility (Fig. 10). Unfortunately statistical inference does not allow to consider these results as
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Fig. 6. Average lead time against inter-arrival time CV (strongly unbalanced mix).
Fig. 7. Average system utilisation against shop layout choice (balanced mix).
Fig. 8. Server needed against mix balancing.
55
56
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
Table 11
Mean values for e$ciency and agility indexes
E$ciency
Agility
Layout
Job
FCS
Flow
32.7818
18.4445
6.9569
1.4459
11.3107
14.8392
I-a variability
0%
40%
100%
19.4673
19.4190
19.2969
17.2102
9.2331
1.1525
Mix balancing
High
Medium
Low
23.3302
19.0858
15.7671
5.0043
6.1863
16.4052
Fig. 11. Macro performances against lots inter-arrival time
variability.
driven on work centres bu!ers "lling dynamics.
While lots inter-arrival time increases, the #uctuation range of queues content increases heavily and
irregularly over time. Such an e!ect implies a significant worsening of system agility mean value, as
plotted in Fig. 11. So, aiming to answer research
questions 2 and 3, it can be stated that demand
variability has a strong e!ect only on system agility
and not on e$ciency performances. Besides, this
e!ect is due to lots inter-arrival time variability and
not to mix balancing.
6. Conclusions
Fig. 9. Macro performances against shop layout design.
Fig. 10. Macro performances against mix balancing.
both signi"cant and generalisable, so that it is not
possible to state anything about the impact of such
factors on system performances. With regard to lots
inter-arrival time variability, data in Table 11 show
that system e$ciency does not depend in any way
on this factor. On the contrary, the impact on
system agility is very heavy because of the e!ect
In this paper three alternative manufacturing
layouts (job shop, #exible cell shop, #ow shop) are
compared aiming to test their robustness with
respect to system input variability, modelled
through the variation coe$cient of lots' inter-arrival time to the system and unbalancing of product
mix. Results obtained by means of simulation experiments show that trade-o! between set-up saving and routing #exibility has a well evident impact
on system macro performances: agility and e$ciency. Set-up saving allows to reduce in-queue waiting
time, routing #exibility allows to minimise process
resources assigned to the manufacture system. So
alternative layout con"gurations have opposite behaviour. Job shop process layout involves a system
resource organisation which provides best e$ciency performances (average utilisation, utilisation
balancing, number of servers needed,2), since the
design criterion imposes absolute priority to resources minimisation allowing to have maximum
routing #exibility. In #ow shop layout, dedicated
A. D+Angelo et al. / Int. J. Production Economics 68 (2000) 43}57
nature of resources allows maximum set-up saving
but, at the same time, provides routing in#exibility.
So having the best queue related performances
(work in process, average bu!er size, average bu!er
time, lead time,2) it is necessary to revert to e$ciency. Obviously, hybrid forms should provide
intermediate performance. Flexible cell shop layout
ensures an intermediate level of set-up saving and
an intermediate level of routing #exibility, i.e. an
intermediate level of e$ciency and agility.
Considering di!erent demand variability conditions, in terms of inter-arrival time variation and
mix balancing, the experimental analysis proved
that the larger e!ect on system performances is due
to the "rst factor, not to the second one. However,
this e!ect is limited to agility performances (queue
and time related performances). System e$ciency
seems not to depend on demand variability.
Such results indicate that manufacturing layout
choice depends on the particular performance considered critical by the decision maker. There does
not exist a layout that overcomes others in an
absolute way. Each one is characterised by a proper
level of routing #exibility and set-up saving.
With respect to literature indications, this paper
returns consistency to the hypotheses of cellular
manufacturing layout choice, which guarantees intermediate level of system e$ciency and agility.
Empirical evidence shows that layout alternative
to job shop or, in general, functional forms, almost
always brought a signi"cant improvement in overall system performance. The need to solve this
contradiction between theoretical models and empirical research, points out that in comparative
theoretical studies (like the presented one), some
critical factors are not considered, such as labour,
labour}machine interaction, run time (and not only
set-up) reduction, inter-cell movement, not instantaneous movement, and so on.
So, this study would not mind a methodological
contribution to the choice of the best manufacturing layout, due to the data-dependent nature of
system performance, but it proposes some clarifying elements for the decision making process which
companies apply when they decide to implement or
convert their manufacturing layout.
Results obtained in this research have to be
checked for a more complex manufacturing system.
57
Authors are performing a sensitivity analysis to test
signi"cance of part-machine matrix size on presented research questions. It would be interesting also
to remove the hypotheses of zero move time and
quantify the e!ect of other demand related variables, such priority assignment for families scheduling, rules for bu!er scheduling and so on.
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