The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
IMDS
108,6
Effects of product design
on assembly lines performances
A concurrent engineering approach
726
Received 26 November 2007
Revised 26 February 2008
Accepted 11 March 2008
Antonio C. Caputo and Pacifico M. Pelagagge
Department of Mechanical, Energy and Management Engineering,
University of L’Aquila, L’Aquila, Italy
Abstract
Purpose – The paper’s aim is to assess the impact of product related features on the performances of
assembly line manufacturing systems, also providing a specific Design for Manufacturing and
Assembly rating index to assess the goodness of a product design solution with respect to assembly
line performances.
Design/methodology/approach – A computer simulation-based parametric analysis was carried
out to assess the impact of four major product-related parameters. 216 different assembly line balance
problem instances were evaluated. Findings allowed to develop a DFMA rating index specific for
assembly line manufacturing as well as design guidelines.
Findings – Assembly sequence degrees of freedom and the ratio of the average task duration to the
maximum duration are the most influencing parameters. While the former should be maximized, only
a moderate task duration variability was found beneficial. The influence of other factors resulted less
marked and changing on a case-specific basis.
Research limitations/implications – Complex interactions between product design features and
line performances prevent generalization. The performed numerical experimentation, although
extensive, remains somewhat limited respect all possible practical situations. The proposed rating
index should be utilized while maintaining an overall perspective about the mutual influence of all
parameters. Some suggested guidelines imply a trade off with traditional DFMA guidelines.
Practical implications – Product designers are given useful insights, tools and guidelines to
develop better producible products. With the proposed ranking index a designer can easily rate his
choices when selecting assembly tasks and sequences, as well as rank alternative product designs
solutions.
Originality/value – The paper presents an original discussion about the impact of product design
choices on assembly line performances. The developed DFMA rating index and guidelines are new.
Keywords Design, Assembly, Assembly lines, Product design
Paper type Research paper
Industrial Management & Data
Systems
Vol. 108 No. 6, 2008
pp. 726-749
q Emerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570810883987
1. Introduction
Product innovation and the ability of quickly developing products and bringing them
to the market are key factors in maintaining globally competitive a manufacturing
company (Drejer, 2008; Hanninen and Kauranen, 2007; Khan et al., 2007; Lee, 2007;
Smith and Reinertsen, 1991). During new products development, the simultaneous
involvement of different functions and overlapping of their activities, along with better
communication between departments, has proven to reduce development time, reduce
cost and increase quality giving rise to the concurrent engineering concept (Hundal,
1997). In fact, traditionally, design and manufacturing activities have taken place
sequentially rather than simultaneously leading to inefficient and time consuming
iterations between design and manufacturing stages (Shukor and Axinte, 2007).
Concurrent engineering philosophy (Nevins and Whitney, 1989; Parsaei and Sullivan,
1993; Smith, 1997) advocates, in fact, to carry out simultaneously the product and
process design with the aim of minimizing the product life cycle cost and the time to
market while providing high added value products for the consumer (Prasad, 1996;
Seetharaman et al., 2007). In this framework, Design for Manufacturing and Assembly
(DFMA) techniques are widely employed (Boothroyd et al., 1994; Gupta et al., 1997;
Bralla, 1999). However, the traditional DFMA approach essentially focuses on
obtaining a product with a high level of manufacturability. DFMA, in fact, attempts at
minimizing production cost through simplification of product structure mainly
resorting to a reduction of parts count, proper selection of the best combination of
materials, geometry and cost-effective manufacturing methods for all parts, and
simplification of manual assembly tasks. This implies that most DFMA techniques
rate a product design on the basis solely of direct manufacturing and assembly costs.
No further reference is given instead to the overall impact that the manufacturing tasks
and product structure have on the performances of the entire production system,
including planning and control issues. In fact, the review of literature in this field
shows an absence of a methodology for effectively incorporating the concerns of
production into the early design stages, and that most of the design for manufacturing
approaches do not consider constraints related to operations of manufacturing
systems, but rather deal with manufacturing processes (Govil and Magrab, 2000;
Kusiak and He, 1997). This is quite a limitation as many product design variables, such
as tolerance levels, assembly tasks sequence constraints, utilization of bottleneck
resources, degree of variability of materials flows and process times, etc. significantly
affect design, operation and management of the manufacturing system, with direct
consequences on WIP levels, lead times and machines utilization (Bramall et al., 2003;
Caputo and Pelagagge, 2006; Corti and Portioli-Staudacher, 2004; Govil and Magrab,
2000; Kusiak and He, 1997; Soundar and Bao, 1994). All these factors bear a cost as well
as an adverse effect on system performances, which traditional DFMA techniques fail
to account.
In order to contribute to a solution of this problem, in a previous work Caputo and
Pelagagge (2006) proposed an innovative design for production (DFP) methodology
relying on a rating index which allows to carry out a more exhaustive comparative
ranking of design alternatives based on manufacturing system performances as
affected by product features. The index can also be utilized to supplement and extend
traditional DFMA ranking techniques by looking at parameters previously neglected.
In this work the attention is focused instead on assembly lines production systems in
order to assess the impact of product related features on balancing efficiency and
consequently provide a rating index useful to evaluate the goodness of a product
design solution with respect to assembly line performances. As a result some
guidelines for designers are also given.
The paper is organized as follows. At first, a general discussion about assembly
process complexity as affected by design choices and the impact on line performance
measures is carried out supported by the relevant literature. Then four influencing
parameters are selected, expressing the task duration variability, their relationship
with the cycle time, the number of operations and the strength of constraints in the
assembly sequence. A parametric analysis is then carried out to assess the effects of
Product design
on assembly
lines
727
IMDS
108,6
the considered parameters on the performances of the assembly line. A thorough
analysis of results follows to understand the role of each investigated parameter, to
verify its degree of influence and determine the preferred value. Results of this analysis
are summarized into a set of guidelines for product designers. Finally, a performance
index is proposed to quantitatively assess the goodness of a product design solution
with respect to the assembly line performances.
728
2. Problem statement
Assembly lines are flow-line production systems consisting in a serial arrangement of
N workstations through which workpieces flow at a steady pace. At each station a
certain part of the total work necessary to manufacture the product is performed. The
whole assembly process is made of a number of separate operations (tasks). Each task
is characterized by a task duration Ti, while a set of precedence constraints, imposed
by technological restrictions, usually define the ordering in which the tasks may be
performed. Line throughput dictates the maximum time available at each station to
perform the allotted tasks (i.e. cycle time, TC). The assembly line balancing problem
involves the assignment of tasks to the workstation so that no precedence constraint is
violated and the sum of operations duration at each workstation does not exceed
the allowed cycle time, while optimising some performance measure so that the line
operates as efficiently as possible. Most commonly the objective is maximizing the
capacity utilization of the line which, in case of deterministic operation times and
single-model production, may be simply expressed as minimize the number of stations
for a given cycle time, minimize the cycle time for a given number of station, minimize
the sum of idle times at the stations or the percentage of idle times, equalize the levels
of capacity utilization at the stations and minimize flow time. However, cost and profit
oriented goals are also widely adopted.
Therefore, common performance measures to be optimized are the line efficiency h
(percent)
P
Ti
h¼
ð1Þ
N TC
which measures the capacity utilization, the balance delay time, BD, which measures
the unused capacity and is equal to the sum of idle times at the stations:
X
BD ¼ NT C 2
Ti
ð2Þ
the balance delay ratio BDR (percent), being h ¼ 1 2 BDR, measuring the percent
capacity inutilization:
P
NT C 2 T i
ð3Þ
BDR ¼
NT C
and the balance efficiency BE (percent) measuring the equality of distribution or
workload among the stations:
P
jS k 2 S AVG j
BE ¼ 1 2
ð4Þ
N S AVG
where Sk is the total work time at k-th station and SAVG the average station workload.
The literature concerning assembly lines balancing issues and techniques is very
large and the interested reader is referred to Scholl (1999) for an in depth analysis.
However, the level of balance efficiency one can expect to achieve in real life
applications depends from the complexity of the actual assembly problem instance
which, in turn, is largely dictated by the product design choices.
In fact, the following product characteristics are likely to affect the effectiveness and
efficiency of line balancing because they impact on the computational problem
complexity (Scholl, 1999; Driscoll and Thilakawardana, 2001):
.
Number of tasks. The higher the number n of tasks to be performed the higher
will be the number of workstation and the number of feasible task sequences to
be explored (neglecting precedence constraints there are n! feasible task
sequences). Therefore, the problem complexity is expected to grow exponentially
with increasing n, but the higher number of possible sequences may increase the
likelihood of finding an efficient one.
.
Task times. When few precedence constraints apply, a high variability of task
times may enable better combination of tasks to form station loads with lower
idle times respect the case of almost similar operation times. When instead the
assembly sequence is strongly constrained a low variability of task times may
improve the load uniformity among workstations.
.
Task times and cycle time. If task times are small respect cycle time there should
be more station loads with low idle times because it is easier to combine small
items than large ones to fit within the cycle time constraint.
.
Precedence constraints. In general the higher the number of precedence
constraints the lower will be the number of feasible solutions, which might
reduce the computational complexity, but less likely will be to find an efficient
solution.
Additional useful insights come from the work of Kilbridge and Wester (1961) who
early recognized the relationship among the various problem parameters. According to
their experimental work comparing the balance efficiency of four different products
manufactured by distinct companies, it was verified that balance delay generally has a
sharp increase at very small cycle times and monotonically decreases when cycle time
increases, except in one case, characterized by some peculiar restrictions to the
placement of fixed facilities. In that case a cycle time minimizing the balance delay was
found. However, no significant impact of the actual tasks duration distribution was
observed in the examined cases. The study of Kilbridge and Wester also pointed out
that care should be taken in choosing a cycle time that divides evenly into the total
work content time. If this relationship is not considered, and a cycle time is arbitrarily
chosen, the resulting balance delay may be high, regardless of the size of the cycle time.
Their evidence thus suggests that high balance delay is associated with a wide range
of work element times, a high degree of inflexible line mechanization and the
indiscriminate choice of cycle time.
To assess such issues in a quantitative manner a number of Authors tried to present
simple numerical measures of complexity for the assembly line balance problem
(Talbot et al., 1986).
Product design
on assembly
lines
729
IMDS
108,6
730
Mastor (1970) proposed the order strength OS parameter, i.e. the ratio of the number
of ordering relations that exist in the assembly graph to the possible number that could
exist, to measure the relative strength of precedence relations. Problems with a large
OS are basically expected to be more complex than such with small OS values.
However, this parameter only describes the number of precedence relations but not
their structure.
Dar-El (1973) introduced two distinct indices, the Flexibility ratio FR ¼ 1 2 OS,
and the West ratio, WR, as the average number of tasks per station. The latter
criterion, however, needs to produce a balanced line before WR can be evaluated.
Problems with small FR and WR tend to be more complex.
Wee and Magazine (1981) introduced the time interval TI ¼ [Tmin/TC, TMAX/TC] as
the interval bounded by the ratios of minimum and maximum task duration to the
cycle time, to account for the range of task times respect the cycle time. A small length
of TI indicates that task times vary in a small range. Therefore, problems are expected
to be increasingly complex if TI is small and is near to the right border of [0, 1] meaning
that the average task duration is similar to the cycle time. However, this index neglects
the distribution of actual task times. Scholl (1999) describes the time variability ratio as
TV ¼ Tmax/Tmin. This has the advantage of not mentioning the cycle time which
makes it an intrinsic measure of the time structure of the precedence graph rather than
of single problem instances. Problem complexity is expected to grow with decreasing
values of TV, which indicate that operations times vary in a small range or that the
minimum task time is large.
Portioli (1999) introduced the Parallelism Index:
IP ¼
1 n Li þ U i
‡
n i¼1
2
ð5Þ
to measure the average number of degrees of freedom (DOF) available in the
assignment of a task to a station given the precedence relationships existing among
tasks. In equation (5) n is the number of operations and Ui the number of DOF at step i
when the assembly sequence is built by selecting at each step the operation that yields
the largest number of DOF. Li is the number of DOF at step i when the sequence is built
by selecting at each step the operation that yields the smallest number of DOF. It can
be expected that the higher the DOF the better because a more effective line balancing
is possible. Resorting to simulation experiments Portioli shows that given a number of
operations and an average task time the percent idle time reduces when both the PI and
the cycle time increase because the higher DOF and smaller task times enable a better
fitting assignment of tasks to stations. The same happens when the PI is fixed but
average task time is reduced respect the cycle time.
Johnson (1988), however, observes that the above measures only describe a single
characteristic of the problem at a time and advises to choose a combination of
measures, while Scholl (1999) points out that a suitable measure of complexity should
incorporate information about the structure of the precedence graph together with the
task times. To this end Driscoll and Thilakawardana (2001) introduce an aggregate
precedence index:
PI ¼
PS þ PB
2
ð6Þ
where PS is the precedence strength index defined as PS ¼ (c 2 1)/(n 2 1) being c the
number of precedence columns in the precedence matrix and n the overall number of
tasks, while PB ¼ Cav/c is the precedence bias, being Cav the average element column
position. PS represents the constraints on element selection during balancing, with
extremes represented by weakly ordered diagram with no constraints and strongly
ordered diagram totally sequentially constrained. PB is the precedence bias and
represents the existence of available elements for assignment early in the balancing
process and is a measure of the precedence diagram variability.
To account for task time variability in reference to cycle time, instead, Driscoll and
Thilakawardana (2001) introduce the task time index:
TTI ¼
TR þ TD
2
ð7Þ
where TR ¼ TAVG/TC is the ratio of average task duration to cycle time:
2sT
TD ¼ 1 2
TC
ð8Þ
is the task time distribution index with sT being the standard deviation of task times.
A high value of TR means an average task duration similar to cycle time which makes
balancing more difficult, while a small value of TD means a great variability of task
times with the presence of small work elements improving the ability to “pack” tasks
into stations. Overall TTI and PI may be combined into a compound project index
Pjt I ¼ (PI þ TTI)/2.
Summing up it can be stated that intuition and empirical evidence might suggest
that the performances of an assembly line production system in general are more likely
to improve when:
.
the number n of assembly operations is large (the number of stations grows but
better balancing occurs thanks to the greater choices when assigning tasks to
stations and the greater number of alternative sequences to be explored);
.
tasks duration is highly variable, with a strong presence even of tasks of short
duration (except when the assembly sequence is strongly ordered);
.
the average task duration is small respect cycle time; and
.
the assembly sequence has many DOF.
This also means that an increased computational complexity, as far as the specific
problem instance is concerned, often may lead to an improved balancing efficiency.
However, while assembly line designers are given such input data by the design
bureau or the process planners, product designers instead have the opportunity of
shaping the value of such influencing parameters enabling improved performances of
the manufacturing system. Therefore, DFMA techniques should be improved in order
to explicitly account for assembly line designers needs. As an example in a product
design the tasks could be subdivided into smaller tasks to reduce the average task time
respect cycle time, more task duration variability could be designed and more flexible
assembly sequences could be devised.
Product design
on assembly
lines
731
IMDS
108,6
732
3. Analysis methodology
To assess the impact of the product design features of the performances of an assembly
line the following methodology was adopted.
At first four distinct product design features were chosen, as representative of the
complexity of assembly line balancing instance, namely:
(1) the number n of assembly tasks to be performed;
(2) the average number of DOF in the assignment of a task to a station given the
precedence constraints in the assembly sequence;
(3) the ratio of average task time to the maximum task time TAVG/TMAX (Figure 1);
and
(4) the ratio of the maximum task time to the cycle time TMAX/TC (Figure 1).
The DOF represents the average number of choices one has in selecting the next task to
be assigned to a station after a given task (i.e. a node in the precedence graph) has been
assigned. It is computed as expressed in equation (5). It may vary in the interval:
h Xn21 i
i =n
ð9Þ
1; 1 þ
i¼1
T
Overall the four selected variables address all of the parameters recognized as
influencing in the literature on assembly line balancing (Dar-El, 1973; Driscoll and
Thilakawardana, 2001; Johnson, 1988; Mastor, 1970; Portioli, 1999; Talbot et al., 1986),
i.e. the number of tasks, the order strength, the task duration variability, the ratio of
task times to the cycle time. Furthermore, they may be considered as equivalent to the
various indices utilized by other authors to quantify such parameters. In fact
parameters TAVG/TMAX and TMAX/TC may be considered to be equivalent to
parameters TD and TR of Driscoll and Thilakawardana (2001) and to the time interval
of Wee and Magazine (1981), while the ratio TMAX/TC has been explicitly considered by
most of the cited authors. Even the task number has been generally adopted in the
literature. The average number of DOF, measured through the index IP of Portioli
(1999), may be considered roughly equivalent to index PS of Driscoll and
Thilakawardana (2001) or to OS of Mastor (1970) and FR suggested by Dar-El
(1973). However, while most authors utilized only a subset of such indices at a time,
only Driscoll and Thilakawardana (2001) utilized, as happens in this work, a complete
set of parameters. Furthermore, while other product features could be certainly utilized
Figure 1.
Sample tasks duration
distribution and indication
of characterizing
parameters
∆T
TMAX
TC
TAVG
Tasks
to characterize an assembly problem instance, i.e. tolerance levels, task complexity,
buffers availability, technical constraints of work stations and so on, such specific
factors are best utilized for detailed characterization of specific situations, but are not
amenable to provide generalised results. Therefore, they will not be considered in the
following analysis.
An experimental campaign was then carried out by changing the values of each
parameters, one at a time, in order to consider all combinations and evaluate the
assembly line performances basing on the following measures: line efficiency h,
balance efficiency BE, and the ratio of the actual to theoretic number of stations
SR ¼ N/NT.
As far as the number of task is concerned three levels were considered, four levels
were considered for DOF, three levels were assumed for the ratio of average to
maximum task time, and six levels for the ratio of the maximum task time to the cycle
time. It should be noted that TAVG/TMAX and TAVG/TC cannot be changed
independently because the further constraint TMAX # TC must be satisfied. Then,
K 2 ¼ T AVG/T C ,
the
condition
indicating
K1 ¼ T AVG /TMAX,
TMAX/TC ¼ K2/K1 ¼ K3 # 1 translates in the condition K1 $ K2. The resulting
experimental matrix is shown in Table I, where the adopted parameters values give
rise to the following values for the TAVG/TC ratios: 0.16, 0.2, 0.24, 0.3, 0.32, 0.36, 0.4,
0.48, 0.54, 0.6, 0.64, 0.72, 0.8.
The selection of levels for the examined parameters has been made in order to
uniformly span across the entire variability range allowed by the parameters, but
limiting their number to a manageable value in order to avoid a combinatorial
explosion of the number of experiments to be carried out. The values of DOF are
instead concentrated in the first half of the variation range because in that area the line
balancing efficiency degrades rapidly and it is necessary to make a more detailed
analysis. The levels values were chosen to be comparable with those assumed by most
other authors and are expressed in relative manner for sake of generality except the
number of tasks. In greater detail the maximum value of task number, n ¼ 30,
compares well with that of Mastor (1970) which ranged between 20 and 40 and Portioli
(1999) who assumed 20 tasks. Dar-El (1973) and Talbot et al. (1986) chose slightly
larger values between 50 and 100 tasks, while only Johnson (1988) examined a very
wide range, from 20 to 1000. As far TMAX/TC is concerned Talbot et al. (1986) examined
values from 0.5 to 1, while Johnson (1988) from 0.1 to 0.6 (expressed as station to work
element ratio). With reference to the strength of precedence relations most authors
(Johnson, 1988; Mastor, 1970; Talbot et al., 1986) adopted OS values roughly in the
range 0.2-0.8.
Overall 216 different combinations of parameters values were examined. Actually
3 £ 4 ¼ 12 different assembly processes were compared each characterized by a
different precedence graph obtained increasing in three levels the number of nodes
from 10 to 30 and for each number of nodes changing the precedence constraints in
N
DOF
TAVG/TMAX
TMAX/TC
10; 20; 30
DOF min; 0.25 DOF max; 0.5 DOF max; DOF max
0.4; 0.6; 0.8
0.4; 0.5; 0.6; 0.8; 0.9; 1
Product design
on assembly
lines
733
Table I.
Experimental matrix
showing the assumed
valued of examined
parameters
IMDS
108,6
734
order to span the entire DOF variability range. Then for each precedence graph the line
balancing was carried out for all 18 possible combinations of TAVG/TMAX and
TMAX/TC ratios. This two-step approach was justified considering that the number of
operations and DOF define intrinsically the complexity of the instance as well as the
precedence graph. Then the subsequent variation of time-related parameters explores
the effect of changing the distribution of task duration and the externally imposed
productivity goals (i.e. through cycle time variations).
To solve each problem instance a computer model was developed performing line
balancing adopting the ranked positional weight technique (Helgeson and Birnie, 1961).
For each of the 216 examined problem instances, 100 replications, obtained by
randomly changing task times (while respecting the specified TAVG/TMAX and
TMAX/TC ratios), were utilized to compute average values of the performance measures
in order to gain statistical consistency.
4. Parametric analysis results
4.1 Effect of operations number
In the examined range of operations number (10-30) line efficiency h shows a steady
increase as the number operation grows and in general the improvement is more marked
the greater is the DOF of the precedence graph and the lower is the TAVG/TMAX ratio.
This confirms previous literature findings and may be explained by the greater average
number of choices of feasible tasks to saturate the workstations when n (and
consequently the absolute value of the maximum DOF), increases especially when the
average task duration is short respect cycle time. As an example Figures 2 and 3 show
the effect of task number on line efficiency for two values of average DOF.
As far as the balance efficiency BE is concerned, in average a similar improvement
trend with increasing n was observed although less marked. Passing to the SR ratio
instead, mixed results were obtained depending on the other parameters values,
making any generalization difficult. The main finding was that SR might improve
when n increases but only when DOF is large and the TAVG/TMAX ratio is small.
As an example Figures 4 and 5 show the trend of SR values when the number of tasks
increases, with the maximum DOF value and two values of the TAVG/TMAX ratio.
1.00
Efficiency
0.90
TMAX = 0.4 Tc
TMAX = 0.5 Tc
TMAX = 0.6 Tc
TMAX = 0.8 Tc
TMAX = 0.9 Tc
TMAX = Tc
0.80
0.70
Figure 2.
Effect of tasks number on
line efficiency for different
DOF
DOF = 0.25 MAX
TAVG 0.4 TMAX
0.60
10
20
N
30
1.00
Product design
on assembly
lines
TMAX = 0.4 Tc
TMAX = 0.5 Tc
TMAX = 0.6 Tc
Efficiency
0.90
TMAX = 0.8 Tc
TMAX = 0.9 Tc
735
TMAX = Tc
0.80
0.70
DOF = MAX
TAVG = 0.4 TMAX
0.60
10
20
30
N
Figure 3.
Effect of tasks number on
line efficiency for different
DOF
1.60
TMAX = 0.4 Tc
1.50
TMAX = 0.5 Tc
TMAX = 0.6 Tc
1.40
TMAX = 0.8 Tc
TMAX = 0.9 Tc
SR
1.30
TMAX = Tc
1.20
1.10
DOF = MAX
TAVG = 0.4 TMAX
1.00
0.90
10
20
30
N
Computed results also show that passing from n ¼ 10-30, all the line performances
increase more markedly when TMAX/TC ¼ 0.5 respect all other cases. This is a
misleading circumstance caused by the value assumed by the [(TAVG £ n)/TC] ratio
which defines the theoretic minimum number of stations. When the theoretic minimum
number of stations is an integer any slightest balancing inefficiency causes the need for
adding at least one more station with an abrupt decrease of line performances. Instead
when the theoretic minimum number of stations is obtained from rounding in excess a
real number it is easier to maintain the actual number of station equal to the minimum
one or to have a better balancing efficiency.
A synthesis of the obtained results is depicted in Table II.
4.2 Effect of degrees of freedom
With reference to the influence of the number of DOF in the assembly sequence, both
line efficiency h and the SR parameter steadily improve with an increase of DOF
Figure 4.
Effect of tasks number on
SR for different DOF
IMDS
108,6
1.60
TMAX = 0.4 Tc
DOF = MAX
TAVG = 0.8 TMAX
1.50
TMAX = 0.5 Tc
TMAX = 0.6 Tc
1.40
TMAX = 0.9 Tc
SR
736
TMAX = 0.8 Tc
1.30
TMAX = Tc
1.20
1.10
1.00
Figure 5.
Effect of tasks number on
SR for different DOF
0.90
10
20
30
N
DOF
Table II.
Overview of performance
results when operations
number increases
Line efficiency h
Balance efficiency BE
Station ratio SR
Low
High
"
"
,
" "
" "
"
TAVG/TMAX
Low
High
" "
" "
"
"
"
,
TMAX/TC
Low
High
,
,
,
,
,
,
Notes: " ¼ improves; " " ¼ significantly improves; , ¼ mixed results
thanks to the wider choice in the assignment of tasks to stations. This also confirms
previous literature findings. The improvement is more marked when the number of
tasks in the assembly sequence increases because this increases also the upper bound
DOF max. However, it was noticed that while performance improvement is marked
when passing from DOF min to 0.5 DOF max, it becomes negligible passing from
0.5 DOF max to DOF max. This diminishing returns result is similar to that noticed by
Portioli (1999). As an example Figures 6 and 7 show a sample trend of h and SR when
the average number of DOF increases from the minimum to the maximum value.
No generalized conclusion can be given instead for the balance efficiency. In fact
BE is more stable, respect variations of TMAX/TC, when both n and TAVG/TMAX are
high, while it gets unstable when TAVG/TMAX is low and n is high. This happens
because with increasing n a greater number of stations will be required and
the unbalance of single stations gets more easily masked when computing the average
balance efficiency of the line. With reference to DOF, a higher DOF can even be
counterproductive for BE. In fact, with increasing choices it can be easier to balance
properly one station and leave other stations poorly balanced. The possibility of
having good efficiency at single stations but poor overall line balancing is enhanced in
case of low values of TAVG/TC, and TAVG/TMAX (i.e. many short duration tasks easily
assignable when DOF is high). This unstable behaviour is especially noticeable when
TMAX/TC is about 0.4. As an example Figures 8 and 9 show BE trends vs DOF.
Obtained results are summarized in Table III.
Product design
on assembly
lines
1.00
Efficiency
0.90
TMAX = 0.4 Tc
0.80
737
TMAX = 0.5 Tc
TMAX = 0.6 Tc
0.70
TMAX = 0.8 Tc
N = 30
TAVG 0.4 TMAX
TMAX = 0.9 Tc
TMAX = Tc
0.60
Min
0.25 Max 0.5 Max
Max
DOF
Figure 6.
Effect of DOF on line
efficiency and SR
1.60
TMAX = 0.4 Tc
1.50
N = 30
TAVG 0.4 TMAX
1.40
TMAX = 0.5 Tc
TMAX = 0.6 Tc
TMAX = 0.8 Tc
TMAX = 0.9 Tc
SR
1.30
TMAX = Tc
1.20
1.10
1.00
0.90
Min
0.25 Max 0.5 Max
DOF
Max
4.3 Effect of TAVG/ TMAX
This parameter is representative of the distribution of tasks duration. High values of
TAVG/TMAX indicate a high frequency of long duration tasks and comparatively few
short duration tasks. Simulation results show that line efficiency increases with a
decreasing TAVG/TMAX ratio, especially when n and DOF are high (i.e. it is preferable to
have many short duration operations but with a high DOF in order to saturate easily the
workstations) as expected from previously reported literature findings. When instead
the assembly sequence is relatively inflexible (DOF is low) it is preferable to have a high
TAVG/TMAX ratio (i.e. similar operations mostly of high duration) as already suggested
by Kilbridge and Wester (1961). In fact in this case the presence of short tasks but with
strong sequence restrictions frequently yields poorly saturated stations because
assigning short and long tasks in a forced order may lead to exceed the TC constraint,
while if tasks are long then the assignment sequence is less important.
Figure 7.
Effect of DOF on line
efficiency and SR
IMDS
108,6
1.00
0.90
0.80
BE
738
TMAX = 0.4 Tc
0.70
TMAX = 0.5 Tc
TMAX = 0.6 Tc
TMAX = 0.8 Tc
0.60
N = 20
TAVG 0.4 TMAX
TMAX = 0.9 Tc
Figure 8.
Effects of degrees of
freedom on balance
efficiency
TMAX = Tc
0.50
Min
0.25 Max 0.5 Max
Max
DOF
1.00
0.90
BE
0.80
TMAX = 0.4 Tc
TMAX = 0.5 Tc
0.70
TMAX = 0.6 Tc
TMAX = 0.8 Tc
0.60
Figure 9.
Effects of degrees of
freedom on balance
efficiency
TMAX = Tc
0.50
Min
0.25 Max 0.5 Max
Max
DOF
N
Table III.
Overview of performance
results when DOF
number increases
N = 30
TAVG 0.8 TMAX
TMAX = 0.9 Tc
Line Efficiency h
Balance efficiency BE
Station ratio SR
Low
High
"
,
"
" "
,
" "
TAVG/TMAX
Low
High
,
,
,
,
,
,
TMAX/TC
Low
High
,
,
,
,
,
,
Notes: " ¼ improves; " " ¼ significantly improves; , ¼ mixed results
Figure 10 shows sample line efficiency variations when the TAVG/TMAX ratio is
increased.
In average the balance efficiency was found to improve with increasing values of
TAVG/TMAX ratio but there appears to be poor correlation between such two parameters.
DOF = Min
1.00
DOF = 0.25 MAX
DOF = 0.5MAX
Efficiency
0.90
Product design
on assembly
lines
DOF= MAX
739
0.80
0.70
N = 30
TMAX 0.9 Tc
0.60
0.4
0.6
0.8
TAVG / TMAX
Figure 10.
Effect of average task
duration on line efficiency
When TAVG/TMAX is high BE is in general less influenced by the number of DOF, while if
TAVG/TMAX is low then BE is affected by DOF especially when TMAX/TC is low. In such
cases a high DOF may be counterproductive as discussed above.
Passing to SR a similar behaviour has been observed, fairly irrespective of the value
of n and DOF, when TAVG/TMAX increases. When TMAX/TC ¼ 0.4, then SR is rather
stable and gets the lowest (i.e. better) values. When TMAX/TC ¼ 0.5, SR has a
decreasing trend while if TMAX/TC ¼ 0.6(0.8 the trend reverses and becomes
increasing. When TMAX/TC ¼ 0.9 the trend stabilizes and becomes decreasing for
TMAX/TC ¼ 1. In all cases the greater the DOF the better. However, in general, better
SR values are obtained when TAVG/TMAX is small. As an example Figures 11 and 12
show opposing SR trends for different values of TMAX/TC ratios.
Overall the influence of TAVG/TMAX is questionable and may have mixed effects on the
assembly line performance measures. However, it may be in most cases preferable to have
low values of TAVG/TMAX ratio. A synthesis of the obtained results is shown in Table IV.
1.60
DOF = Min
1.50
DOF = 0.25 MAX
1.40
DOF = 0.5MAX
DOF= MAX
SR
1.30
1.20
1.10
N = 20
TMAX 0.5 Tc
1.00
0.90
0.4
0.6
TAVG / TMAX
0.8
Figure 11.
Effect of average task
duration on SR
IMDS
108,6
1.60
DOF = Min
1.50
DOF = 0.25 MAX
DOF = 0.5MAX
1.40
SR
740
DOF= MAX
1.30
1.20
1.10
N = 20
TMAX 0.8 Tc
1.00
Figure 12.
Effect of average task
duration on SR
0.90
0.4
0.6
TAVG / TMAX
N
Table IV.
Overview of performance
results when TAVG/TMAX
increases
Line efficiency h
Balance efficiency BE
Station ratio SR
0.8
DOF
Low
High
Low
High
#
"
,
# #
"
,
"
"
,
# #
"
,
TMAX/TC
Low
High
,
"
,
,
"
#
Notes: " ¼ improves; # ¼ worsens; # # ¼ significantly worsens; , ¼ mixed results
4.4 Effect of TMAX/ TC
This parameter is affected by the product characteristics and the productivity goal of
the line. Therefore, it is representative of a specific line balancing instance. Usually the
product designer does not know the value of TC and, for a given product, an optimal TC
may exist or not.
When line efficiency is considered, better performances are generally obtained for
the extreme values of TMAX/TC (i.e. 0.4 and 1) while h worsens for intermediate values
of TMAX/TC. In the first case this happens because being TMAX, and therefore TAVG,
small respect TC it is easier to saturate stations given the small duration tasks, while in
the latter case at least one station can achieve 100 per cent efficiency. This effect is
shown for example in Figure 13.
Passing to the balance efficiency, BE generally decreases when TMAX/TC increases
in cases of low to medium DOF. When medium-high DOF occur an oscillation of BE
may occur with wider amplitude especially with the higher DOF and the lower
TAVG/TMAX ratio. This effect is shown for example in Figures 14 and 15.
As far as the SR ratio is concerned, results depend strongly from the value of the
TAVG/TMAX ratio. Often the lowest SR is obtained for the minimum value of TMAX/TC.
Higher SR values are obtained with TMAX/TC . 0.4 but non monotonously increasing.
In fact, for the cases of TAVG/TMAX ¼ 0.4 and 0.6 a peak of SR is observed at
TMAX/TC ¼ 0.5, while for TAVG/TMAX ¼ 0.8 the peak is observed at values of
1.00
TAVG = 0.4 TMAX
TAVG = 0.6 TMAX
TAVG = 0.8 TMAX
Efficiency
0.90
Product design
on assembly
lines
741
0.80
N = 30
0.70
DOF = 0.25 MAX
0.60
0.4
0.5
0.6
0.7
0.8
0.9
1.0
TMAX / Tc
1.00
Figure 13.
Effect of maximum task
duration on line efficiency
TAVG = 0.4 TMAX
TAVG = 0.6 TMAX
TAVG = 0.8 TMAX
0.90
BE
0.80
0.70
N = 30
DOF = Min
0.60
0.50
0.4
0.5
0.6
0.7
0.8
0.9
1.0
TMAX / Tc
TMAX/TC of 0.6 or 0.8. SR amplitude variations decrease with increasing n but the DOF
value seems not significant. As a general rule TMAX/TC should be minimized but the
value TMAX/TC ¼ 0.5 should be avoided. As an example Figures 16 and 17 show
SR values as a function of TMAX/TC when different values of average DOF and number
of tasks apply. As a general rule small TMAX/TC values improve line performances
especially when DOF is low.
A synthesis of the obtained results is given in Table V
Overall the above results confirm the general trends that can be ascertained by the
existing literature (Kilbridge and Wester, 1961; Mastor, 1970; Portioli, 1999; Talbot
et al., 1986), namely that:
.
as the mean and maximum task time approach the cycle time the line balancing
becomes more difficult (i.e. TMAX/TC should be kept low);
.
for a given cycle time, the larger the number of small elements available and the
fewer the large elements, the easier line balancing becomes (i.e. TAVG/TMAX and
Figure 14.
Effect of maximum task
duration on balance
efficiency
IMDS
108,6
1.00
0.90
BE
742
0.80
0.70
N = 20
DOF = MAX
0.60
Figure 15.
Effect of maximum task
duration on balance
efficiency
0.50
TAVG = 0.4 TMAX
TAVG = 0.6 TMAX
TAVG = 0.8 TMAX
0.4
0.5
0.6
0.7
0.8
0.9
1.0
TMAX / Tc
1.50
TAVG = 0.4 TMAX
TAVG = 0.6 TMAX
TAVG = 0.8 TMAX
1.40
SR
1.30
1.20
1.10
N = 10
1.00
DOF = 0.25 MAX
Figure 16.
Effect of maximum task
duration on SR
0.90
.
.
.
0.4
0.5
0.6
0.7
TMAX / Tc
0.8
0.9
1.0
TMAX/TC should be kept low and the lower the ratio of long tasks to short tasks
the better);
irrespective to average task time the presence of small task time elements
improves the ability to pack task into stations thus improving balance efficiency;
the higher the strength of precedence relations the harder line balancing becomes
(i.e. the more parallel is the assembly graph the better is line balancing); and
balancing performances improves when number of tasks increases.
Nevertheless, detailed findings of this work are not directly comparable with other
literature results as this investigation markedly differs from those reported previously.
Literature mostly focuses on developing effective balancing algorithms and evaluating
their performances instead of systematically evaluating the sensitivity of line
performances to parameters variations. For instance, variation of problem instances
(i.e. changing number of tasks, order strength and cycle times) only were utilized to
1.50
TAVG = 0.4 TMAX
TAVG = 0.6 TMAX
TAVG = 0.8 TMAX
N = 30
DOF = 0.5 MAX
1.40
Product design
on assembly
lines
SR
1.30
743
1.20
1.10
1.00
0.90
0.4
0.5
0.6
0.7
0.8
0.9
Figure 17.
Effect of maximum task
duration on SR
1.0
TMAX / Tc
N
Line efficiency h
Balance efficiency BE
Station ratio SR
DOF
Low
High
Low
High
,
#
,
,
#
,
,
#
,
,
,
,
TAVG/TMAX
Low
High
,
,
,
,
#
,
Notes: # ¼ worsens; , ¼ mixed results
assess the goodness of the utilized balancing technique when determining the minimum
number of work stations for a given cycle time or computing the minimum cycle time for
a given line length (Mastor, 1970; Talbot et al., 1986). In this work, instead, changes in the
assembly problem instances are parametrically investigated in order to assess their
impact on line performances, while utilizing the same assembly line balance procedure.
5. Guidelines for designers
From the above discussed results some practical guidelines for product designers can
be ascertained.
5.1 Average degree of freedom
The higher the DOF the better. However, DOF values greater than 0.5 DOF max yield
no further increasing benefits in practice. A diminishing returns effect holds as already
noticed by Portioli (1999). Therefore, designers should allow at least moderate
flexibility in the assembly sequences of the products, but not necessarily strive for
excessive flexibility which, anyhow, might not be obtainable in practice. Anyway this
appears to be the most sensible parameter.
5.2 TMAX/TC ratio
The smaller it is the better. This is to avoid that in case of an inflexible assembly
sequence the balance efficiency gets penalized. However, when a suitable degree of
Table V.
Overview of performance
results when TMAX/TC
increases
IMDS
108,6
freedom exist in the assembly sequence (i.e. DOF . 0.25 DOF max) then the value of
TMAX/TC becomes rather ininfluent. This means that designers should aim for short
duration tasks maybe splitting longer tasks into smaller subtasks even if this would
increase the total tasks number which is contrary to traditional DFMA guidelines. In
this case a trade off analysis should be required.
744
5.3 TAVG/TMAX ratio
It is difficult to give general guidelines about this parameter. While intuition would
suggest that this ratio should be low in general, meaning a high variability of task
durations, it was found that its value should be preferably high especially if
DOF , 0.5 DOF max, substantially confirming the early empirical observation of
Kilbridge and Wester (1961). With higher DOF values the TAVG/TMAX ratio becomes
instead scarcely influent on line performances. Only in case TAVG . 0.5 TC (which
hardly occurs in practice) it would be preferable to have small values of TAVG/TMAX
ratio. This means that designers should aim for a greater number of longer duration
task respect shorter duration ones. Even in this case attention is due because increase
of task time (maybe through aggregation of subtasks) is against traditional DFMA
guidelines and the possible impact of longer assembly time on costs is to be evaluated.
5.4 Number of tasks
From the point of view of line performance the higher is the number of tasks the better,
because more opportunities for better balancing result. This could be accomplished by
splitting tasks in subtasks to avoid increasing total assembly time. It should be noted
that increase of task number is opposite to DFMA guidelines which advocate instead
task number minimization. In this case a trade off analysis should be required.
However, recent literature (Whitney, 2004) confirms that assembly work is no longer
considered a major driver of product cost thus relieving criticality of this parameter.
5.5 TAVG/TC ratio
This ratio should be kept as small as possible (i.e. increasing TC or reducing TAVG by
splitting tasks) so that the probability of finding a short duration task to be allocated to
a station with a small residual idle time increases. However, an integer value of
(TC/TAVG) £ N should be strictly avoided otherwise a local minimum of performances
may occur.
Overall such guidelines are aimed at increasing the efficiency of factory operations
by improving resource utilization through increase of assembly flexibility. The latter,
in fact, contributes to better utilize workstations, avoid bottlenecks, better distribute
workloads among stations, reduce the number of workstations for a given production
rate or allow to increase productivity for a given line. The increase of DOF besides
giving more chances to develop effective and low cost assembly sequences (Whitney,
2004), allows to design reconfigurable assembly lines thus supporting lean
manufacturing concepts. It may make easier to switch from manual to automated
assembly in case of increased market demand, and enables delayed product
differentiation allowing product customization. The greater flexibility in assigning
tasks to stations, while preserving line performances, is also useful in case
subassemblies are made by several third parties using different facilities and
resources. Furthermore, to reduce task durations respect cycle time and to increase the
percentage of short duration operations allows the opportunity of reducing cycle time
in case it is required to increase throughput and is critical to reduce the defect fractions,
which is recognized to be positively correlated to the average task durations (Hinckley,
1993). Finally, the above guidelines do not deal in detail with product configuration,
i.e. design of single parts for ease of assembly or simplification of product structure by
eliminating unnecessary parts and consolidating multiple parts, which remains the
realm of traditional DFMA techniques.
6. Rating index
In order to provide product design engineers with an easy to use tool to rapidly
assess the goodness of a product design solution with respect to the assembly line
performances, a quantitative ranking index is developed here building on previous
findings and guidelines. This index can be used to carry out comparative ratings
among alternative solutions as well as to supplement traditional DFMA indices.
In a previous work (Caputo and Pelagagge, 2006), the above discussed issues
prompted to define three indices to assess producibility on an assembly line, namely
the station saturation index penalizing the presence of long tasks respect the cycle
time:
SSI ¼ 1 þ
T AVG
TC
ð10Þ
the process time variation index penalising a low variability range of tasks times:
PTVI ¼ 1 þ
T AVG
T MAX
ð11Þ
and the precedence constraints index which penalizes design solutions characterized
by strong precedence constraints among tasks:
PCI ¼ 1 þ
1
PI 2 1
ð12Þ
where PI is the Portioli’s parallelism index. If PI ¼ 1 then PCI ¼ 3 to avoid an
indefinite value.
An overall rating index is then computed as:
RI ¼ SSI £ PTVI £ PCI
ð13Þ
This aggregate index has a minimum value of 1 and the higher it gets the worst is the
producibility score.
However, results from this study enable to gain further insights into this problem
and present an improved rating index to be used in alternative or in addition to the
previously defined index.
The above analysis, in fact, pointed out that the TAVG/TMAX ratio and DOF are the
most influencing product-related parameters. The more the DOF approaches the
maximum number of DOF corresponding to the specified tasks number the better.
Conversely even if some task duration variability is beneficial, an excessive wide range of
tasks duration may make difficult to properly balance the line. An intermediate range of
Product design
on assembly
lines
745
IMDS
108,6
746
TAVG/TMAX with a value around 0.6 yields the best results, with higher values less worse
than lower values. However, the weight of TAVG/TMAX reduces when DOF increases.
Such consideration may be captured by the following ranking index:
T AVG
T MAX
DOFmax 2 DOF
RI ¼ 1 þ 2 0:6
DOFmax
T MAX
T AVG
ð14Þ
The index has the unit value as a lower bound, meaning that the higher the score
the worst is the expected impact of the designer’s choice on the line performance.
In this case one obtains the best value RI ¼ 1 when DOF ¼ DOF max or when
TAVG/TMAX ¼ 0.6. This index may be also used to complement the ranking index
(equation (13)) previously proposed by Caputo and Pelagagge (2006). Such rating
indices enable to assess the impact of each design solution on the overall
performances of the manufacturing system. The lower the RI, the better a design
solution is suited to minimize adverse impacts on the manufacturing system
performances. This also enables a rapid relative comparison among competing
alternative designs.
Finally, the proposed ranking indices may be utilized to supplement the rating
obtained from traditional DFMA techniques which usually include an evaluation of
the components manufacturing and assembly costs only. In fact, the proposed
index focuses on parameters which are undoubtedly relevant, as demonstrated in
the present work, but are overlooked by traditional methods which, instead, focus
on the minimization of part count and operations cost only without analysing the
impact on production system performances. Furthermore, it is also pointed out that
when system performance are accounted for, requirements in conflict with those
coming from traditional DFMA approaches may result. Therefore, a trade-off
decision may arise and the use of complementary ranking methods showing the
interacting effects of the parameters may help designers to make an informed
choice.
7. Conclusions
In this paper a parametric analysis was carried out at first to assess the impact of
product features on the overall performances of assembly lines. While the analysis
largely confirmed previous literature findings, it also pointed out more subtle
interactions between the involved parameters as well as some counter intuitive
behaviours. Overall the necessity of striking a balance looking for a trade-off
between mutually influencing parameters was highlighted. As a consequence,
a rating index was proposed to assess the expected impact of design choices on the
management and operation of the manufacturing system. The proposed index
appears easy to use, requires only a minimal knowledge of the manufacturing
system details, and enables to compare competing design alternatives. This novel
DFP methodology, specifically aimed at assembly lines, can supplement the
traditional DFMA techniques which are focused on the minimization of components
manufacturing and assembly costs but not at optimising the interactions between
product design and production system. Adopting the proposed methodology a
product designer will be able to rate his design choices and incorporate operational
efficiency criteria in the definition of assembly sequences or in the tasks design
phase. To this aim some guidelines for designers were also developed to summarize
the analysis results. However, the reader is advised that the described results as well
as the proposed rating index are based on the analysis of a somewhat limited
number of case studies and that generalization of results should be made cautiously.
In fact the analysis also showed that complex interactions occurring between the
numerous influencing parameters, when facing specific instances of the assembly
problem, may significantly affect the assembly line performances in a hardly
predictable manner.
Therefore, additional research will be needed to analyze in greater detail the
interactions between product design features and performances of manufacturing
systems in order to derive more general results. In particular, future research work will
be aimed at first at extending the parametric analysis (i.e. investigating more complex
problem instances with a larger number of tasks, and evaluating in greater detail
the role of DOF and task times ratios). A quantitative correlation between line
performances and design features will be also searched resorting to response surface
methodology. Furthermore, the effect of additional product features will be
investigated and other performance measures will be considered. Finally, the role of
those design features giving rise to trade off situations will be analysed in greater
detail.
References
Boothroyd, G., Dewhurst, P. and Knight, W. (1994), Product Design for Manufacture and
Assembly, Marcel Dekker, Inc., New York, NY.
Bralla, J.G. (Ed.) (1999), Design for Manufacturability Handbook, McGraw-Hill, New York, NY.
Bramall, D.G., McKay, K.R., Rogers, B.C., Chapman, P., Cheung, W.M. and Maropoulos, P.G.
(2003), “Manufacturability analysis of early product designs”, International Journal of
Computer Integrated Manufacturing, Vol. 16 Nos 7/8, pp. 501-8.
Caputo, A.C. and Pelagagge, P.M. (2006), “Integrating production systems performances in the
DFMA methodology”, in Morgan, M.N. and Jenkinson, I.D. (Eds), Proc. 4th International
Conference on Manufacturing Research, September 4-7, Liverpool, John Moores University,
Advances in Manufacturing Technology.
Corti, D. and Portioli-Staudacher, A. (2004), “A concurrent engineering approach to selective
implementation of alternative processes”, Robotics & Computer-Integrated Manufacturing,
Vol. 20 No. 4, pp. 265-80.
Dar-El, E.M. (1973), “MALB – a heuristic technique for balancing large single-model assembly
lines”, AIIE Transactions, Vol. 5 No. 4, pp. 343-56.
Drejer, A. (2008), “Are you innovative enough?”, International Journal of Innovation and
Learning, Vol. 5 No. 1, pp. 1-17.
Driscoll, J. and Thilakawardana, D. (2001), “The definition of assembly line balancing difficulty
and evaluation of balance solution quality”, Robotics & Computer-Integrated
Manufacturing, Vol. 17 Nos 1/2, pp. 81-6.
Govil, M.K. and Magrab, E.B. (2000), “Incorporating production concerns in conceptual
product design”, International Journal of Production Research, Vol. 38 No. 16,
pp. 3823-43.
Gupta, S.K., Das, D., Regli, W.C. and Nau, D.S. (1997), “Automated manufacturability analysis:
a survey”, Research in Engineering Design, Vol. 9 No. 3, pp. 168-90.
Product design
on assembly
lines
747
IMDS
108,6
748
Hanninen, S. and Kauranen, I. (2007), “Product innovation as micro-strategy”, International
Journal of Innovation and Learning, Vol. 4 No. 4, pp. 425-43.
Helgeson, W.B. and Birnie, D.P. (1961), “Assembly line balancing using the ranked positional
weight technique”, Journal of Industrial Engineering, Vol. 12, pp. 394-8.
Hinckley, M. (1993), “A global conformance quality model. A new strategic tool for minimizing
defects caused by variation, error, and complexity”, PhD dissertation, Stanford University,
Palo Alto, CA.
Hundal, M.S. (1997), Systematic Mechanical Designing: A Cost and Management Perspective,
ASME Press, New York, NY.
Johnson, R.V. (1988), “Optimally balancing large assembly lines with FABLE”, Management
Science, Vol. 34 No. 2, pp. 240-53.
Khan, Z., Bali, R.K. and Wickramasingh, N. (2007), “Identifying the need for world class
manufacturing and best practice for SMEs in the UK”, International Journal of
Management and Enterprise Development, Vol. 4 No. 4, pp. 428-40.
Kilbridge, M. and Wester, L. (1961), “The balance delay problem”, Management Science, Vol. 8,
pp. 69-84.
Kusiak, A. and He, D.W. (1997), “Design for agile assembly: an operational perspective”,
International Journal of Production Research, Vol. 35 No. 1, pp. 157-78.
Lee, C.W. (2007), “The innovation and success of consumer electronics using new product
development process”, International Journal of Innovation and Learning, Vol. 4 No. 6,
pp. 587-611.
Mastor, A.A. (1970), “An experimental investigation and comparative evaluation of production
line balancing techniques”, Management Science, Vol. 16 No. 11, pp. 728-46.
Nevins, J.L. and Whitney, D.E. (1989), Concurrent Design of Products and Processes,
McGraw-Hill, New York, NY.
Parsaei, H.R. and Sullivan, W.G. (1993), Handbook of Concurrent Engineering, Chapman & Hall,
London.
Portioli, A. (1999), “A concurrent engineering approach to assembly line balancing”,
Proc. International Conference on Industrial Engineering and Production Management,
FUCAM, Glasgow, July 12-15, pp. 342-9.
Prasad, B. (1996), Concurrent Engineering Fundamentals: Integrated Product and Process
Organization, Prentice-Hall, Upper Saddle River, NJ.
Scholl, A. (1999), Balancing and Sequencing of Assembly Lines, 2nd ed., Physica-Verlag,
Heidelberg.
Seetharaman, A., Sreenivasan, J., Bathamenadan, R. and Sudha, R. (2007), “The impact of
just-in-time on costing”, International Journal of Management and Enterprise
Development, Vol. 4 No. 6, pp. 635-51.
Shukor, S.A. and Axinte, D.A. (2007), “Manufacturability analysis systems: Issues and future
trends”, International Journal of Production Research, pp. 1-22.
Smith, R.P. (1997), “The historical roots of concurrent engineering fundamentals”, IEEE
Transactions on Engineering Management, Vol. 44 No. 1, pp. 67-78.
Smith, P.G. and Reinertsen, D.G. (1991), Developing Products in Half the Time, Van Nostrand
Reinhold, New York, NY.
Soundar, P. and Bao, H.P. (1994), “Concurrent design of products for manufacturing system
performance”, Proceedings of the 1994 IEEE International Engineering Management
Conference, pp. 233-40.
Talbot, F.B., Patterson, J.H. and Gehrlein, W.V. (1986), “A comparative evaluation of heuristic
line balancing techniques”, Management Science, Vol. 32 No. 4, pp. 430-54.
Wee, T.S. and Magazine, M.J. (1981), “An efficient branch and bound algorithm for an assembly
line balancing problem. Part II: maximize the production rate”, Working Paper No. 151,
University of Waterloo, Waterloo.
Whitney, D.E. (2004), Mechanical Assemblies, Oxford University Press, New York, NY.
Product design
on assembly
lines
749
Corresponding author
Pacifico M. Pelagagge can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints