design of single assembly line for the delayed differentiation of product variants

Design of single assembly line for the delayeddifferentiation of product variants

Tarek AlGeddawy • Hoda ElMaraghy

Published online: 18 January 2011

� Springer Science+Business Media, LLC 2011

Abstract Delayed Product Differentiation (DPD) can reduce the manufacturing

complexities arising due to the proliferation of products variety. A new optimization

model constructs the optimum layout of delayed differentiation assembly lines for a

mix of products to be manufactured by the same system and optimizes the position

of the differentiation points. This model employs a classification tool (Cladistics)

used in biological analysis and modifies it for use in planning DPD assembly lines

configurations in order to incorporate the assembly precedence constraints, required

production rates of different product variants and existing production capacity of

work stations. The optimum layout configuration ensures that the quantities required

of different products are produced on the same line; while achieving balance,

minimizing duplication of stations and maximizing the overall system utilization.

The developed model has been applied to a group of automobile engine accessories

normally assembled on different lines. The use of Cladistics to analyze product

variants that are candidates for delayed assembly is an original approach for

designing the assembly line layout and identifying the best differentiation points. It

also helps rationalize the design of product variants and their features to further

delay their assembly differentiation and achieve economy of scale without affecting

their functionality.

Keywords Assembly � Product variants � Delayed differentiation � Cladistics

T. AlGeddawy (&) � H. ElMaraghy

Intelligent Manufacturing Systems Centre, University of Windsor, Windsor, ON, Canada

e-mail: [email protected]

H. ElMaraghy

e-mail: [email protected]

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Flex Serv Manuf J (2010) 22:163–182

DOI 10.1007/s10696-011-9074-7

1 Introduction

Automotive manufacturers as well as many other industries are under pressure to

offer a range of product variants to satisfy customers’ demands, while maintaining

attractive prices and responsive delivery times to remain competitive. Therefore,

managing the augmented product varieties is becoming a major concern in

manufacturing in general and in the automotive industry in particular. Fujimoto

et al. (2003) discussed many sources of product and process varieties in automotive

industry;

• Different basic functions within the same family of products (example:

crossover car models might offer different wheel drives to select from such as

4WD for sport utility or FWD for family ride).

• Adaptation of the same function to different requirements (example: car engines

with the same output might vary in dimensions and layout based on the car

model).

• Offering optional functions (examples of options include: power/manual

windows, sun-roof, passenger side air bags, fog lights…etc.)

• Non-functional requirements (examples include: manufacturing processes

variation may arise due to the introduction of a new technology like replacing

control knobs with touch buttons or navigation screens; which are all executing

the same functions).

In general; offering a wide range of product variants can lead to a considerable

expansion in the number of sub-assemblies and amounts of raw materials that must

be kept in stock to satisfy the full range of possible variations in product

configurations (Bragg 2004). In addition, customer service cost increases when

many product variations exist. For example, managers attempt to stock as much as

possible of the finished products under the pressure of maintaining good customer

service quality, while eventually those stocks might become obsolete in a relatively

short time due to rapid technology changes (Lee and Billington 1994). Increasing

the product variety means increasing manufacturing complexity, which eventually

leads to higher managerial burdens in order to handle all the previously discussed

symptoms of increasing products varieties, and associated escalating administrative

cost.

These negative effects of products variety proliferation motivated the adoption of

postponement strategy, which exploits the commonality between produced items

and designs the production and distribution processes to delay the point of products

differentiation. Delaying product differentiation can increase flexibility, reduce

uncertainties and decrease the cost of complexity (Shao and Ji 2008).

This paper specifically targets Form Postponement in assembly lines, which

requires deferring product differentiation activities as much as possible along

production stages by delaying the insertion of specialized components or performing

variant-specific processes. These specialization and customization production stages

are called points of products differentiation. Consequently, these deferred activities

lead to a Delayed Product Differentiation (DPD) production system (Garg and Tang

1997). Form postponement has been reported in many industries such as: (1) batch

164 T. AlGeddawy, H. ElMaraghy

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process industries of a product form variation i.e. converting hot aluminum to

different sections bars and food industry (Caux et al. 2006); (2) in the low variety—

high volume batch production of specialized high voltage cables; (3) the high

variety—low volume production of electric motors in a process cells layout, and (4)

the high variety—high volume manual assembly of keypads for automated telling

machines (Harrison and Skipworth 2008); assembly of computers and their storage

devices and subassemblies (Swaminathan and Tayur 1999); and in automotive

manufacturing facilities by introducing points of differentiation after body shop and

paint shop to allow sequencing to reduce production disturbances (Fournier and

Agard 2007).

The main enabler of products form postponement is ‘Modularity’, which is a

relative property that depends on the degree of similarity between the physical and

functional product design and architecture and the extent of decoupling between

components (Ulrich and Tung 1991). Many approaches are used to further facilitate

form postponement by capitalizing on the concept of modularity. Standardization of

components is a product redesign strategy that encourages utilizing common

components and interchangeable sub-assemblies (part commonality) or common

processes in order to reduce the manufacturing system complexity. Modular designis also a product re-design strategy which decomposes the whole product into

independent modules that can be easily assembled together. This approach was

considered the main enabler for applying DPD assembly strategy. Process re-

construction, as the name suggests, is a process re-design strategy that refers to re-

sequencing the steps used in making a product, in order to perform the common

process steps shared by multiple products before the product specific process steps

(Lee and Billington 1994; Lee and Tang 1997).

2 Assembly line design

The design of assembly line entails several tasks; including but not limited to;

assembly Line composition into stations associated with the assembly of different

variants features; arranging the stations into a layout according to precedence

requirements and modeling, analysis and optimization to balance out processing

times in different sections and optimize performance measures such as utilization

and stations duplication, and finally, selection of specific workstations, material

handling and transfer systems (De Lit et al. 2003; Rekiek and Delchambre 2006).

2.1 Assembly line balancing

The assembly line balancing mainly involves searching for the optimum assignment

of tasks (nodes), precedence relations (arrows) and task durations (assigned to each

node) in a pre-set assembly graph, to achieve a pre-defined single or multi-objective

goal. These objectives vary from a single objective of minimizing the number of

stations for a given cycle time, or minimizing the cycle time for a given number of

stations in a serial line; to optimizing line efficiency and imbalance simultaneously

in a non-serial line (Rekiek and Delchambre 2006). Boysen et al. (2007) referred to

Design of single assembly line 165

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two types of problems: (1) the simple assembly line balancing problem (SALP)

where assigning tasks to stations in a serial line is performed; and (2) the general

assembly line balancing problem (GALP) where more practical aspects are

considered such as assembly layouts other than serial lines (e.g. U-shape), non-

deterministic process times, varying production demands and the effect of using a

number of parallel stations in single assembly stages on their workload distribution.

They also pointed out the existence of a wide gap between research and practical

applications in the field of GALP, where the real layouts found in industry could not

be considered, were not solved satisfactorily or the results were not applicable due

to the used simplifying assumptions.

For assembly line balancing where product variety exists involves a deeper

elaboration of an initial rough assembly line layout to achieve a desired cycle time.

Two approaches were suggested: (1) a multi-model assembly line where different

product models are considerably distinctive, therefore production is executed in

batches of each product model, and (2) a mixed-model assembly line where the

product model variants are significantly similar that they can be assembled

simultaneously on the same line (Boysen et al. 2007). The latter is usually a simple

serial line called ‘paced’ assembly line where the proportion of each variant in the

total assembly plan is known (Rekiek et al. 2000). A serial line might also possess

several parallel identical stations in a single assembly stage to satisfy a desired cycle

time. It is indicated that for such arrangement; the workload differences increase

drastically among those identical stations when product variety increases in a mixed

products assembly line in addition to the stock allocation problem. Consequently,

continuous production rescheduling and process re-sequencing are needed (Boysen

et al. 2007), besides the need for an optimized assembly stations layout to eliminate

non-value added process elements such as walking and waiting (Klampfl et al.

2005). The serial line can be bent in a U-shaped line for more homogenous

workloads of assembly stations, however, balancing these workloads requires

production scheduling integration with tasks assignment to stations (Miltenburg

2002). The U-shaped line was the main focus of the GALP in past research (Boysen

et al. 2007). Few researchers focused on balancing more complex shapes such as

asymmetric lines (Ko and Jack Hu 2008) and cellular layouts (Lee and Johnson

1991), however the lack of integration of the layout design with line balancing still

exists.

The model proposed in this paper presents an integrated assembly line design

approach where layout design, assembly sequencing, task assignment and stations

location decisions are combined in the optimization process for a mixed-model

product assembly that has a more complex system layout than a serial or a U-shaped

line.

2.2 Delayed product differentiation

When the postponement strategy is applied to assembly processes; it defers the final

configuration of products, thus it is considered a form postponement. In a mixed-

product assembly; the points of products differentiation represent specialization

stages in the assembly system where each product starts to develop its own unique


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identity, thus, becoming differentiated from other end product designs in the

family. The determination of these points has traditionally been associated with

some strategic factors such as capital investment and inventory costs as well as

work in process level (WIP). The determination of these points is considered a

pre-requisite for all of the subsequent assembly line design tasks. Garg and Tang

(1997) modeled a three stage assembly system with two points of differentiation

as a distribution system under both centralized and decentralized control policies.

A decision is made to choose either early family postponement (delaying the first

differentiation point) or late product postponement (delaying the second differ-

entiation point) based on demand variability, lead time correlation and inventory

savings.

Some mathematical models are found in the literature for determining the optimum

positions of points of product differentiation in an assembly line. Hsu and Wang

(2004) presented a dynamic programming model to locate such points. The model

decides at every design stage, based on lead-time, capital investment, processing, and

inventory holding costs, whether to continue with the common part assemblies or to

introduce a specific differentiating component. Another dynamic programming model

was presented by Chen et al. (2006) to determine the product differentiation points.

The model is similar to Hsu and Wang’s model, as it works only for a family of

products with the same production processes type and precedence constraints. In

addition to the cost of investment, processing, and inventory holding, other cost

components were taken into consideration, such as transportation cost. Fournier and

Agard (2007) introduced a practical automotive paced serial production line with two

points of differentiation, where buffers of WIP are installed so that units in those

buffers can be reordered before the next step, which improved the earliness and the

lateness of the entire production system.

Swaminathan and Tayur (1998) introduced a different model which starts from a

number of products having common components, and proposes the best semi-

finished products configurations (vanilla boxes), and provides the right amount and

mix of these vanilla boxes inventory level to minimize the total stock-out and

holding cost. These vanilla boxes are equivalent to the work-in-process at each

differentiation point, and their number is decided by the user. In their later model

(Swaminathan and Lee 2003) the assembly sequence problem was taken into

consideration. The applied precedence constraints were component-based, i.e. they

show the relationship between each pair of used components but they do not follow

the precedence constraints for each end product, consequently the precedence

constraints were greatly relaxed, and while the final results included feasible

assembly sequences, no physical assembly line layout was synthesized. In addition,

the number of vanilla boxes (points of differentiation) is still pre-set, and was

limited to two in their examples.

Two main shortcomings are observed in the previous models:

1. There is no mention of the physical assembly or production system layout in

most cases, but a serial line type that produces end products and keeps WIP at

each differentiation point is assumed.

2. Assembly line balancing was mostly neglected.


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The first short coming was addressed by the proposed model of AlGeddawy and

ElMaraghy (2009), which presented a design process for assembly lines that delays

products differentiation considering only precedence constraints and producing the

best layout accordingly. However, that model did not consider line balancing and its

effect on the optimal line layout. This paper proposes a model that addresses line

balancing and integrates it with the layout optimization process where the main goal

is adopting DPD strategy to postpone the differentiation of products configuration

and assembly stages as much as possible while maintaining a common assembly

line configuration and processes as long as possible. This strategy reduces the risks

associated with increasing products varieties, by exploiting the commonalities

among their variants in components and processes while complying with the

assembly precedence constraints of each product. In addition; the required

production rates of the different products and the production capacities of the

different assembly stations are also taken into consideration since it is believed it

does not only affect the balancing of the assembly line but also its layout and the

locations of products differentiation points. The case study of the car engine

accessories is a good example of a product family the members of which share

functional similarities and common assembly processes that are currently produced

on separate assembly lines, which increases the number of redundant processes and

reduces the overall utilization and efficiency of production. The proposed model

applies form postponement to this car engine accessories assembly by establishing

an optimum assembly line layout that delays product differentiation and minimizing

processes redundancy.

3 Modeling the layout of a balanced delayed differentiation assembly line

The proposed model is divided into two steps; layout construction and layout

evaluation. First; a layout that represents one of the possible delayed differentiation

assembly lines is constructed; showing the locations of the differentiation points.

Second; the commonality analysis and precedence constraints are applied followed

by assembly line balancing. The main objective of the model is to design an

assembly line that satisfies the production requirements of the given product

variants and delays their differentiation while using the minimum number of

assembly stations as described in the next sections.

3.1 Commonality cladistics analysis

In this part of the model; studied products common features are recognized and

products are classified graphically using a novel method utilizing the comparative

data analysis in biological science known as ‘Cladistics’. This classification method

was originally introduced and developed by Hennig (1966, republished in 1999) to

generate cladograms, which are tree-like structures representing evolution course

hypotheses. In manufacturing; Cladistics was used for the first time (ElMaraghy

et al. 2008) to analyze products commonality. A case study of automotive engine

blocks was analyzed, to identify their design evolution trend and obtain more


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informative products groupings than what would be possible using the traditional

product families. These cladograms were also found to represent the candidate

assembly line layouts in the proposed model, since cladogram trees combine the

studied entities based on their common characters under unified branches in those

trees. It was established that the schematic layout of an assembly system that delays

production differentiation resembles the appearance of the cladogram tree. Such

layout would also maximize the postponement of the production of different product

features as much as possible in the layout branches, while producing the common

features along the previous unified branches. The process of constructing the

product variants cladogram begins with choosing end-taxa—which are the product

variants to be assembled, placed at the end of cladogram terminals such as taxa A,

B, C, D and E shown in Fig. 1, then determining the characters that provide

relationships evidence (1–6 in Fig. 1). The different assembly processes of the

studied products will be the characters in the case of assembly line design. Next, all

inherited character states in each taxon are identified. A character state could be

‘primitive’, where that character does not exist or has a low profile (0), or could be

in a ‘derived’ state; representing the existence of the character, or has a more

developed profile (1). The objective of the construction process is to generate the

most parsimonous (minimum length) cladograms. A cladogram length is the number

of the characters that appear on the cladogram graph. The cladogram shown in

Fig. 1 has a total length of 10 steps, while there are only six characters because

some characters are repeated, which represents cladistic conflicts that increase its

length. The objective of the optimization process is to minimize those conflicts so

that the number of characters is kept to a minimum. This is consistent with the

objective of minimizing the total number of assembly stations in the layout so that

the maximum number of common assembly processes/stations can be unified in

joint assembly stages.

Fig. 1 Constructing a cladogram in a parsimony analysis


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3.2 Precedence constraints

The precedence assembly constraints for each product variant are individually

represented by a precedence graph for each. A combined precedence graph is the

main way to represent precedence constraints for the whole product variants/family,

which is then converted into a relationships precedence matrix (Ko and Jack Hu

2008). This step entails grouping common tasks (Boysen et al. 2009); which is an

extra calculation burden. This is, however, avoided in the model proposed in this

paper. Individual precedence graphs are converted into a precedence matrix by

assigning a value of ‘1’ to direct or later succession; else a value ‘0’ is assigned

where no relationship exists. Figure 2 shows the effect of incorporating precedence

constraints on the cladogram construction process. If there is no precedence

constraint; a character (2), which represents an assembly step, can be combined into

one process if it exists in two or more products (e.g. A and B in Fig. 2a). This has

the effect of minimizing the number of duplicate assembly stations in the assembly

system. However, if a precedence constraint exists between character (2) and

another one (1) it prevents their integration; then process combination is not

possible and the corresponding products will require their own separate assembly

processes for such features/characters as shown in Fig. 2b.

3.3 Production requirements

In this section; production rate, R, refers to the system objective to produce a

required number of product units per unit time. Production capacity, Q, is the

system-specific property representing the maximum number of product units that

can be produced per unit time. The objective is to achieve the targeted production

rates of each product variants when balancing the assembly system. Those rates

present the demand constraint that should be achieved by each assembly station.

It is assumed that a number of parallel stations may be installed at an assembly

station as needed to fulfill the required production rate at this particular stage

(Fig. 3). This assumption ensures that the production requirements are satisfied at

each stage regardless of the system layout, which is globally optimized by the

model. The number of parallel stations for a given process is N C R/Q, and the total

production capacity of this stage = N 9 Q. Where N is the ceiling integer (R/Q),

Fig. 2 Applying precedenceconstraints to a cladogram.Products A and B undergoprocess 2. a Processes 1 and 2are independent. b Process 1precedes process 2


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R ¼P8i Ri is the total number of products per unit time required at that stage and Q

is the production capacity (products/unit time) of a single work station.

3.4 System utilization

It can be proven that the utilization of a manufacturing system having a delayed

differentiation layout is always the same or better than that of other types of layouts.

System utilization is simply the used portion of the total system production capacity.

For simplicity and illustration; a two products case is shown in Fig. 4. Product A

and B have required production rates RA and RB, respectively. Only one assembly

operation is required for both products A and B production, which is performed

using a processing station of capacity Q. Only two types of layouts can be used for

this simple case: (a) two separate lines layout (case -a), and (b) an integrated

delayed differentiated line (case -b). For case (a); the required production rates of

each product are fulfilled separately. This implies that a number of processing

stations (NA and NB) of total production capacity—equal to or exceeds the

individual product requirement (RA and RB)—must be used in each line. For case

(b); the number of stations is determined to fulfill the aggregated production

requirements of both products. The system utilization for both cases can then be

calculated using Eqs. 1 and 2.

Ua ¼ ðRA þ RBÞ=½Q� ðNA þ NBÞ� ð1ÞUb ¼ ðRA þ RBÞ=ðQ� NÞ ð2Þ

The number of stations in case (a) imposes the upper limit of needed stations, while

the number of stations in case (b) is always the lower limit; since:

*N ¼ ceiling integer ðRA þ RB=QÞ ð3Þ)NA þ NB ¼ ceiling integer ðRA=QÞ þ ceiling integer ðRB=QÞ ð4Þ

Fig. 3 Fulfilling productionrequirements at each assemblystage


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*Ceiling integer ððRA þ RBÞ=QÞ�Ceiling integer ðRA=QÞþ Ceiling integer ðRB=QÞ ð5Þ

)N�NA þ NB ð6ÞThe utilization of case (b) line is always better than case (a) as long as

N \ NA ? NB. The comparison between case (a) and case (b) utilization in

different cases is shown by Eqs. 7–9

For N\NA þ NB and N ¼ ðRA þ RBÞ=Q! Ub ¼ 100% ð7ÞFor N\NA þ NB and N [ ðRA þ RBÞ=Q! Ub [ Ua ð8Þ

For N ¼ NA þ NB ! Ub ¼ Ua ð9Þ

The previous equations indicate that a delayed differentiation layout would always

be preferred over other layout types not only for minimizing the number of the used

stations but also for maximizing the system utilization. The previous conclusion

entails that an optimum balanced delayed differentiation layout would always yield

the maximum system utilization. The overall utilization of a manufacturing system

can be calculated using Eq. 10.

U ¼X

8ijRij=

X

8ilQil ð10Þ

where: Rij is the required production rate of product (j) at location (i). And Qil is the

production capacity of station (l) at location (i).System utilization is also inversely proportional to idle time. Better system

utilization means more use of available production resources and less idle time in

the system.

3.5 Balanced layout optimization algorithms

The proposed model constructs and evaluates the layout using two algorithms that

are executed iteratively. The evaluation algorithm analyzes the layout topology

(T) generated by the construction algorithm, given a specific group of product

variants expected at the end points of the assembly line (terminals of the

corresponding cladogram).

Fig. 4 Effect of layout on line balancing. a Separate lines. b Grouped stations in delayed productsdifferentiation


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3.5.1 Layout construction

The layout construction algorithm involves the application of the physical graph

constraints on a chosen topology. These constraints connect the graph nodes to

establish the tree structure of the delayed differentiation layout, by imposing the

necessary graph arcs (minimum number of arcs at the upper nodes and increasing

number of arcs at lower nodes). The tree construction part of the algorithm ensures

that all tree terminal nodes are eventually connected to the upper-most node. If a

node (i,j) on the graph is connected by an arc to one of its consecutive lower nodes;

then Xij = 1 else Xij = 0. Some arcs might be redundant which distorts the tree

shape by producing arc loops; hence, they are removed by the connectedness

redundancy removal part of the algorithm. The layout generation algorithm is

shown in Fig. 5.

3.5.2 Layout evaluation

The layout evaluation algorithm shown in Fig. 6 is responsible for distributing

assembly processes over the generated layout and calculating the production

capacities needed to fulfill the production requirements and balance the assembly

line. Steps 1–6 are for data setup and initialization. Step 7 combines common

features in pair-wise products unless this violates a precedence constraint, which is

checked in step 8. Step 8 also balances the assembly line by calculating the

minimum number of parallel identical stations required to perform a process at a

certain stage. Step 13 evaluates the current assembly line, while step 14 stores the

best obtained results.

Finding the most parsimonious cladogram is NP hard combinatorial problem

(Kitching et al. 1998) and consequently the problem of identify the best evaluated

layout. Therefore the choice of the optimization search techniques depends on the

problem size where: (1) Exhaustive search is used for small size problems of 10

variants or less, (2) Random destinations—where a random portion of the solution

space is selected for evaluation—is suitable for problems with 11–20 variants, and

(3) Genetic Algorithm is recommended for large size problems of more than 20

product variants. The presented case study (size of five variants) was solved using

Fig. 5 Algorithm for generating feasible assembly line layout topologies


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exhaustive search within the solution space; consequently the provided solution is

an exact optimum since it was the best found one compared to all other feasible

solutions. The other two techniques use meta-heuristics that are known to find near

optimal solutions.

4 The case study

There are many examples of mixed-products manufacturing in the automotive

industry. A limited number of automotive engine family variants may serve a

handful of automobile models; however the engines accessories vary widely

according to the specific car models. The utilization of a serpentine flat belt that

connects many components of the engine is becoming the common practice since

the 70’s (Cassidy et al. 1979). Several factors led to such variety; engine torque

load, required damping and bearing characteristics among other factors (for more

details on these factors refer to Ulsoy et al. 1985). These accessories are usually a

pair of idler and belt-tensioner in each engine (Fig. 7). The common practice in

Fig. 6 The evaluation and optimization algorithm of assembly line layout


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industry is to assemble these accessories using separate manual, automatic or hybrid

assembly lines depending on features and production volume. The assembly of these

accessories consists of a range of insertion, fastening, aligning and press fitting

processes. In some cases, where accurate alignment is required, a fine surface

machining is used.

A set of five different engine accessories are studied (Fig. 8). These are normally

produced using different assembly lines, and they will be considered for production

by a single system utilizing the concept of delayed differentiation. SAT01, ABT03

and NAT04 are three belt-tensioners with damping mechanisms; while EXI02 and

SAI04 are two idlers without such damping. These products have some common

components and assembly processes that can be explored for possible unification.

Table 1 shows the processes needed to assemble each product. A ‘1’ value means

the process is needed for that specific product; while a ‘0’ value indicates that it is

not required and may be a candidate for amalgamation.

A pulley assembly (1) is placed in the pallet then a bushing (2), which exits in

some variants, is inserted in the pulley. The pulley assembly is held together by

driving a bolt (3.1) or pressing a pivot (3.2). Different engine belt-tensioners may

have U (4.1) or N-shaped (4.2) arms—the latter needs to be inverted upside down

for further operations. The damper (6) is placed inside the arm housing and secured

by pressing and twisting the cup and spring assembly (6) into the arm housing. If the

product is self-aligning; an insert (7.1) is placed on top of the damper before the cup

and spring are assembled. For products requiring accurate alignment (7.2); a CNC

machine is used to make a final precision surface machining of the arm housing

where it gets assembled to the engine. The required production rates for each

product and the production capacity of the assembly stations used for each process

are given in Table 2. The precedence constraints of these assembly processes are

represented by a group of precedence graphs as shown in Fig. 9. These constraints

are converted to a precedence matrix (Table 3) representing the pair wise

relationships between the different assembly processes. A ‘1’ value indicates the

existence of a precedence relationship; while a ‘0’ value means its absence.

Fig. 7 The locations of engine accessories in a car engine


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Same production rate for each task is assumed regardless of the product variant

since a common assembly process that is shared by many variants is by definition

not product variant-specific. At stations where differentiated product variants

assembly takes place, the process time may be different except for duplicate

identical stations that are sometimes set up in different sections to achieve the

Fig. 8 The group of studied engine accessories

Table 1 The assembly processes of the studied products

Place

pulley

Insert

bushing

Fastening Arm positioning Twist cup

assembly

Insert

damper

Align

Bolt Pivot U N Self Fixed

1 2 3.1 3.2 4.1 4.2 5 6 7.1 7.2

EXI02 1 1 0 1 0 0 0 0 0 0

SAI04 1 0 0 1 1 0 0 0 1 0

SAT01 1 0 1 0 0 1 1 1 1 0

ABT03 1 0 1 0 1 0 1 1 0 1

NAT04 1 0 0 1 1 0 1 1 0 1

Table 2 Required production rates and stations production capacities

Product EXI02 SAI04 SAT01 ABT03 NAT04

Required rate (unit/min) 1.1 0.8 1.1 1.1 1.2

Assembly station 1 2 3.1 3.2 4.1 4.2 5 6 7.1 7.2

Capacity Q (unit/min) 2.1 1.9 1.2 1.2 1.6 2.1 2.7 1.5 1.5 1.4


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delayed differentiation. Given the similarity that often exits between product

variants, the similarity of processing times for similar assembly processes is thought

to be a reasonable approximation for the purpose of synthesizing the assembly line

DPD layout. Besides; station duplication is to be minimized through the objective

function of the proposed optimization algorithm.

5 Results

5.1 The assembly line layout

For a set of five end product variants; there exist seven different Cladistic topologies

and 5! = 120 end products-to-terminals combinations. The complete solution space

contains 7 9 120 = 840 possible cladograms, which is feasible to search exhaus-

tively given its small size. The optimum cladogram was found to have a length of 22

steps land two differentiation points after station ‘1’ (3 parallel stations)—where

pulleys are assembled—and station ‘4.1’ (2 parallel stations)—where the U-shaped

arms are positioned in the assembly. The optimum cladogram is shown in Fig. 10a.

This graph can be readily converted to a schematic assembly line layout (Fig. 10b)

Fig. 9 The precedence graphsof the automobile engine belt-tensioners

Table 3 The precedence matrix of the automobile engine belt-tensioners

Predecessor

process

Successor process

1 2 3.1 3.2 4.1 4.2 5 6 7.1 7.2

1 0 1 1 1 0 0 1 1 1 1

2 0 0 0 1 0 0 0 0 0 0

3.1 0 0 0 0 0 0 1 1 1 1

3.2 0 0 0 0 0 0 0 0 0 1

4.1 1 0 1 1 0 0 1 1 1 1

4.2 1 0 1 0 0 0 1 1 1 0

5 0 0 0 0 0 0 0 0 0 1

6 0 0 0 0 0 0 1 0 0 1

7.1 0 0 0 1 0 0 1 0 0 0

7.2 0 0 0 0 0 0 0 0 0 0


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by deleting the arcs that do not possess characters, and rejoining their end nodes.

This schematic layout is then converted to the physical assembly line layout shown

in Fig. 11.

Fig. 10 The optimum assembly line schematic. a Cladistic representation. b Schematic representation

Fig. 11 The optimum and balanced delayed differentiation layout of the five auto-engine accessoriesassembly system


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5.2 Sensitivity analysis

5.2.1 Infinite capacity assembly stations

Infinite capacities of assembly stations are assumed in order to test the dependency

of the optimum layout on the extent of commonality and precedence constraints

among product variants. This assumption relaxes the assembly line balancing

problem, since each station would be capable of satisfying any required production

rate of any given product. This assumption is equivalent to setting every station

capacity Qi to a large value, where:

Qi�X

8jRj; 8i ¼ 1. . .N; 8j ¼ 1. . .n ð11Þ

N: number of processing stations, n: number of products.

In this case; Qi C (1.1 ? 0.8 ? 1.1 ? 1.1 ? 1.2) = 5.3 unit/min. The resulting

unbalanced optimum layout—which does not consider production rate—is found to

be the same as the obtained balanced optimum layout. This result indicates that the

precedence constraints in this case study are highly restrictive. In general it can be

concluded that only smaller number of certain types of layout and stations

arrangement would be feasible, regardless of the required production rate, due to the

limitations imposed by the precedence constraints.

5.2.2 Critical parameters and solution robustness

It is helpful to determine the production rates that affect the layout. Since there are

five products in this case study; five cases were evaluated by the model; in each case

a range of production rates of a specific product is examined. Production rate

ranging between 0.5 and 1.5 units/min is assumed for each of the five products, one

at a time, and a solution is obtained in each case. Table 4 Shows the production

ranges of the different products obtainable by using the original system layout

solution. Production outside these production rate ranges produces alternative

optimum solutions A, B and C of Table 4; which are shown in Fig. 12.

The results show that only products SAI04, ABT03 and NAT04 assembly is

sensitive to production rate change. These three products are processed using

Table 4 Solution robustness measures

Rates are

in unit/min

Original

production

range

Critical

lower

value

Best

solutions

Critical

upper

value

Best DPD

assembly layout

EXI02 0.5–1.5 – Original – Original

SAI04 0.6–0.9 0.5 Original & A 1 Original & A&B&C

SAT01 0.5–1.5 – Original – Original

ABT03 0.9–1.2 0.8 Original & A 1.3 Original & A&B&C

NAT04 0.9–1.3 0.8 Original & B 1.4 Original & A&B&C


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common (the same) assembly stations before one differentiation point (station 4.1—

arm positioning) in the original solution (Fig. 10). The results also identify station

4.1 as being a bottleneck.

It can be noticed that the alternate layout solutions A, B and C do not propose

better delayed differentiation assembly layouts by altering processes sequence, since

the given precedence constraints do not allow much sequence alteration. The results

also show that there is more than one layout that match the original solution but do

not exceed it regarding minimizing the number of assembly stations or maximizing

the system utilization.

6 Conclusions

The proliferation of product varieties is mandated by competition and customer

demands. This trend is clearly evident in the automotive industry where each car

model has many options; consequently the constituent modules and subassemblies

have greater variety. This is well demonstrated in the example of car engine

accessories where families of products that exhibit wide variety exist; yet they have

many common functions, components and assembly processes. An assembly line

designed to postpone their form differentiation would exploit their similarities;

hence, reducing the negative effects resulting from their wide product variation.

A new method has been developed to integrate assembly line layout construction

with the assembly line balancing tasks. A novel model for synthesizing the

schematic layout of DPD assembly systems was introduced. The model adapts

Cladistics—a comparative data analysis tool used in Biology to establish evolution

course of living organisms—to conduct commonality analysis of the studied

products. This cladistics analysis was modified to incorporate assembly processes

precedence constraints and the required production rates for each variant.

Production capacities of the different assembly stations are considered in order to

determine the number of identical parallel stations required at each assembly stage

to fulfill the required production rates of various variants. The developed model was

applied to groups of product variants that have some common features and/or

assembly processes to be produced on a single assembly line/system. The balanced

layout model produced an optimum balanced assembly line layout for their delayed

products differentiation. Examining the assembled product variants cladogram

Fig. 12 The alternative solutions (layouts) in Table 4


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identifies the candidate products features that could be made common in order to

combine and unify as many of the assembly stages as possible and postpone and

reduce the product-specific portions of the assembly system.

Sensitivity analysis indicated that the obtained optimum layouts are more

governed by the precedence constraints than by the required production rates of the

studied products. The sensitivity analysis also has identified the products and

stations that contribute to the superiority of the obtained layout and the ranges of

production rates where that solution outperforms all others.

A family of automobile engine accessories was used as an example of products

that exhibit demonstrable diversification and customization of the end product

according to the target application. Although the main components of each product

are essentially not different; it is their combination in different configurations that

results in the large variety of product models. The developed classification using

cladograms and optimization model successfully illustrated the use of mixed-model

combined assembly lines/systems compared to the dedicated single-model assembly

lines, commonly used in this industry due to the typically large production volumes,

to achieve the benefits of mass-customization through delayed product assembly

differentiation.

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Author Biographies

Tarek AlGeddawy graduated in Production and Mechanical engineering in 1999 from Cairo University,

Egypt, where he also received the M.Sc. degree in Industrial engineering in 2004. He joined the

Intelligent Manufacturing systems Centre (IMSC) as a Ph.D. candidate in 2006, at the University of

Windsor in Ontario, Canada. In 2008, he received a 3-years post-graduate research grant from the Natural

Sciences and Engineering Research Council of Canada (NSERC). His research activities are focused on

management of products variety/change and synthesis of manufacturing systems. He published 6 journal

and 10 conference papers.

Hoda ElMaraghy is a Professor of Industrial & Manufacturing Systems Engineering and the founding

Director of the Intelligent Manufacturing Systems (IMS) Centre at the University of Windsor. She holds a

Canada Research Chair in Manufacturing Systems since 2002. She obtained a Master and PhD degrees in

Mechanical Engineering from McMaster University where she became a Professor and founding Director

of its Flexible Manufacturing R&D Centre until she joined the University of Windsor in 1994 as the first

woman Dean of Engineering in Canada. Professor ElMaraghy is a Fellow of the International Academy of

Production Research (CIRP), Society of Manufacturing Engineers (SME), Canadian Society of

Mechanical Engineers (CSME) and Member of the American Society of Mechanical Engineers. She

serves on several international advisory boards and editorial boards of high impact international journals.

Professor ElMaraghy’s research interests include: Manufacturing Systems Flexibility, Reconfiguration

and Changeability, Products and Production Systems Co-Evolution, and Process and Production

Planning. She has over 380 publications and has contributed to training more than 100 highly qualified

personnel.


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design of single assembly line for the delayed differentiation of product variants

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