Implications of interface management for modularity degree

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6 Received 12 May 2010 Revised 6 October 2010 30 March 2011 Accepted 21 August 2011

Implications of interface management for modularity degree A.H.M. Shamsuzzoha, Yohanes Kristianto and Petri Helo Department of Production, University of Vaasa, Vaasa, Finland Abstract Purpose – The purpose of this paper is to demonstrate modularity degree in terms of interfaces and innovation. Design/methodology/approach – The research objective is achieved through a modeling approach for deciding modular architecture and its implementation regarding unique components and product innovation. A case example is presented to elaborate on the concept of modularity degree and provide an option for choosing the best module from different alternatives. Findings – The presented approach can be considered a product design strategy, in which loose coupling is achieved through standardized component interfaces. Loosely coupled component interfacing is a prerequisite for developing mass customized products. There needs to be a decision support system to formulate the interfacing in order to achieve maximum benefits. This is illustrated in this paper. Research limitations/implications – The modeling strategy for measuring the modularity level is formulated theoretically. This approach needs to be validated through an empirical study in order to generalize its findings. Practical implications – In the industrial arena there is a research gap in identifying and measuring the modularity level, which is formulated in the presented approach. It is hoped that this approach will contribute to filling this research gap in the business environment, which would further benefit managers of firms in their corresponding production processes. Originality/value – The unique contribution of this modeling approach is articulated through analyzing product architecture, with a view to interpreting the component interfaces in a more productive way. This formulation triggers the decision-making process in complex product development processes. Keywords Product design, Decision making, Product development, Component interface, Innovation, Unique component, Decision-making process Paper type Research paper

Journal of Modelling in Management Vol. 8 No. 1, 2013 pp. 6-24 q Emerald Group Publishing Limited 1746-5664 DOI 10.1108/17465661311311950

1. Introduction The term modularization, which is nowadays very familiar to manufacturing communities globally, is attracting much more interest than before. The modularization phenomenon basically involves developing individual modules with specific functionalities, which are then mixed and matched to develop end products. Before deciding on any kinds of product development strategies or architecture, manufacturing firms need to analyze the available strategies, such as modular or integral, component commonality, product platform, postponement, etc. according to their ongoing business opportunities. This will enable them to be competitive in global market segments (Hsuan, 1999). Among all these strategic options, modularization is considered an important product development principle that can enhance productivity in many ways. In product

modularity, standardized and interchangeable components or units are used to offer a wide variety of products through a configuration process (Schilling, 2000). In modularity, strong dependencies among specific components or modules are avoided in order to create design flexibility. In modular product architecture, the functional entities of a product can be divided into more manageable parts/modules, which can also be implemented to standardize the designs and engineering (Baldwin and Clark, 1997). This reduces the complexity of the product design and development process through sharing standard processes or operations within a firm. A module developed according to the modularization principle can be defined as a group of components that can be replaced or removed from the product non-destructively as a complete unit. From this definition it is obvious that the degree of components of a system may be disaggregated and recombined according to new configurations. Each of the developed modules has a unique function or set of functions that can be integrated into different systems for the same functional purpose, with only minor modifications (Ulrich and Eppinger, 1995). In product modularity there is a common understanding that modules as building blocks can be combined to offer a comparatively large number of product configurations (Baldwin and Clark, 1999; Garud and Kumaraswamy, 1995; Sanchez, 1995). Product modularity permits the development of loosely coupled modules that can be rearranged with a view to bringing various configuration possibilities to the end customers (Shamsuzzoha and Helo, 2009). This in turn enables interchangeability among different configurations without compromising product integrity (Orton and Weick, 1990; Sanchez and Mahoney, 1996; Bask et al., 2010; Shamsuzzoha et al., 2010). Modular architecture has been considered an important perspective in product innovation research. Strategic interfacing among components and modules is the basis for the innovation perspective where product architecture covers a number of concepts such as product platforms, product families, standardization, etc. (Mikkola, 2006). It is understood that a product is considered a technical system which is composed of a subsystem and interface, and the subsystem is usually a functional module in the modular product. Due to the prospects of product innovation, we have to consider interfacing among these functional modules or components. Modular product architecture results from the integration of a series of self-contained functional units with standard interfaces, manufactured or supplied and assembled as autonomous modules (Helper et al., 1999). This architecture is widely implemented for incremental innovation, where innovative practices are confined mainly to developing modules according to required functionalities. This principle is extensively implemented in different industries such as automobiles (Baldwin and Clark, 1997; Marx et al., 1997; Sako and Warburton, 1999), computer industries, e.g. Dell (Helper and MacDuffie, 2000), aircraft (Sosa et al., 2003), electronics and software (Staudenmayer et al., 2005). In automobile industries especially in the car industry, typical modules at the highest level in the product structure are seats, cockpits, front-ends, headliners, door panels, fuel tanks, etc. (Mercer, 1995; McAlinden et al., 1999). Through modular design and assembly process, entrepreneurs can efficiently perform mass customization strategy and gain market benefits (Sanchez and Mahoney, 1996; Van Hoek and Weken, 1998). Through the introduction of the internet (B2C and B2B), modularity enables mass customization and the build-to-order principle through cross-product and cross-firm standardization of components with standard interfaces. The rest of the paper is

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organized as follows: Section 2 outlines a general literature review based on the relationships between product architecture and innovation, whilst Section 3 presents modularity decision optimization based on modularity constraint, innovation and number of unique components. Section 4 illustrates an analytical model for modularity degree, while Section 5 presents a case example to validate the operability of the model of modularity degree. Section 6 focuses on various managerial issues regarding modular architecture and its effects on product innovation. Finally the paper is concluded in Section 7 with some future research directions. 2. Literature review Product architecture influences the interfaces and interdependencies among components and parts, which contribute to the product innovation process. Both the internal and external interfaces in product architecture play a critical role in product planning and realizing processes. Different components and/or modules that are connected by interfaces can be used to develop different products through replacement, addition and removal of specific modules. These interfaces accommodate connection, transformation and interaction functions among components and/or modules (Mikkola and Gassmann, 2003). The interfaces also address problems related to product changes, product variety, component standardization and product performances (Shamsuzzoha, 2010). Due to an increasing level of complexity in the product development process, it is hard for single firms to provide all the necessary assets, and thus the openness of interfaces dominates systematic compatibilities (Chen and Liu, 2005). Globalization drives the necessity of disintegration and cooperation among firms to gain potential benefits, suggesting that firms are unsuitable to work independently (Parung and Bititci, 2006). The base requirements for a decentralized or globalized production system are enhanced by modular product architecture, which also influences the developmental flexibility among operational activities (Fixson et al., 2005). This flexibility supports the integration of functional requirements with physical components (Ulrich and Eppinger, 1995; Wyatt et al., 2009). There are several issues concerning product architecture or functional mapping of components. Functional mapping concerns standard interfaces from which the concepts of product platforms and product families are evolved. The product platform includes common functional subsystems and interfaces for generating architecture, which creates product families that share interchangeable modules (Dahmus et al., 2001). Both the product platform and product families are the source of core competence for a company (Meyer and Utterback, 1993). Different protocols or rules determine the interacting functions between components or modules and the geometric matching of existing physical connections. The particular interfaces of components or modules construct informative structures that define the necessary output of the product development processes (Sanchez, 1996). Product architecture, which is generally categorized as integral and modular, has an implicit effect on the innovation principle (Ulrich, 1995; Schilling, 2000). In general, products that are fabricated from modular architecture are more innovative than those from integral architecture due to the inherent flexibilities of modular architecture. Product architectures are not static but change over time, often evolving towards a higher level of modularity (Fixson and Park, 2007). In the literature, there is evidence in support of this architectural change, where product architecture evolves from integral to modular (Baldwin and Clark, 2000; MacCormack et al., 2004; Shibata et al., 2005).

This architectural change can originate from changes in both market demand and technological evolution (Schilling, 2000; Jacobides and Winter, 2005). The architectural issue influences many aspects of the product life cycle from the design phase to the recycling of a product. Therefore, careful consideration is required before designing and assessing product alternatives at the very early stages of development (Otto and Wood, 2001). Increasing customer demand for individualized products puts pressure on product architecture to stay competitive in the market. In order to meet market demands and to achieve greater product variety, modular architecture is the best answer. Modular architecture is the key strategy for developing a product platform from where a range of product variants is created. This platform-based variant development requires attribute assignment for components, module combination and simultaneous design of both approaches (Fujita, 2002). This production phenomenon pushes the production system from mass production strategy towards mass customization (Pine, 1993; Meyer and Lehnerd, 1997; Ramdas, 2003). Architectural shifting could also happen due to technological changes in product design and developments, such as new components, new materials or new processes (Fixson and Park, 2007). For instance, in the case of wrist watches the replacement of quartz affects the complete design architecture and production processes (Jacobides and Winter, 2005). Changes in product architecture or interfaces can even be affected by the individual designer or a team of product designers of existing products due to the gaining of new knowledge or expertise over time. The modularization function, as proposed by Mikkola (2007), is explored in order to understand the implications of product architecture modularity, which is essential in presenting product variety. In this research, the modularization function is discussed and simulated in terms of both standard and non-standard components. The relationships between the component or module interfaces are also explained in relation to achieving mass customization. The research presented by Shamsuzzoha (2011) reflects the strengths of component dependency within product architecture in forming cost effective modular product design and development. 3. Modularity decision optimization Before considering the modular design architecture, manufacturers need to evaluate this strategy in terms of design goals and innovation perspectives. To cope with product innovation, we need to formulate the option for modular innovation from which product differentiations are evolved. This modular innovation is important with respect to customer demands and product differentiation degrees. Higher degrees of product differentiation force manufacturers to reduce innovation modularity. This innovation perspective can be defined in terms of modularity constraint (K) and the number of unique components (U) within a production system. The model described in the following subsections shows that the more unique a component within a module or a product is, the more room there is for the module or product to be innovative, and vice versa. From the model, it can also be observed that a higher number of modularity constraints (K) increase the possibility of more innovative products. This innovation model can be explained as follows. 3.1 The relationship between a unique component and interfaces In order to explain the innovation perspective, we formulate the modularity constraint in terms of interfaces between the components or modules, and the number

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of unique components. Suppose we have N number of interfaces for each component or module in order to exploit certain functionalities. Let us also assume that a certain product or product family contains U number of unique components among the total number of components T. These total numbers of components are the combination of both unique components (non-standard) and common or standard components. The percentage of unique components over the total number of components can be expressed as, (U/T )*100. The relationship among modularity constraint (K ), percentage of unique component and the number of interfaces (N ) within a product can be formulated as follows: K ¼ U% · N

ð1Þ

Let us also consider the innovation effort (I ) in terms of K and U. Suppose innovation effort (I ) is represented by K, where higher K makes a firm to intensify higher I by reducing the number of N and U. In order to achieve the optimum number of unique components U within a product or module, we could apply the Laplace transform rule. Thus, we have the following innovation differential: dI ðU Þ K ¼ dU I ðU Þ 1þK

ð2Þ

This differential indicates the incremental innovation effort in terms of modularity constraint (K ) and the number of unique components (U ). Applying Laplace transform to equation (2) and inverting it provides a modified equation as follows: I ðU Þ ¼

K e 2:U · I 0 ðU Þ 1þK

ð3Þ

From equation (3) we can observe that optimizing the value of K/(1 þ K) by minimizing the incremental deviation of DI(U ) ¼ I(U ) 2 I0(U ) at various numbers of unique components consequently terms the design equation as follows: I ðU Þ K e 2:U ¼ 1 ¼ I 0 ðU Þ 1 þ K

ð4Þ

If we apply the logarithmic transformation on both sides of equation (4), we obtain equation (5) as follows: log

K þ1 ¼ log ðU Þ K

ð5Þ

Again, if we put the value of equation (1) into equation (4), we obtain the transformed equation (6) as follows: U 2 N 2 UN 2 1 ¼ 0

ð6Þ

And finally, the maximum value of U can be achieved from the roots of equation (6), which can be interpreted as follows:

U 1;2 ¼

N^

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð N Þ2 þ4 · ð N Þ 2ð N Þ

ð7Þ

Equation (7) indicates the relationship between the number of unique components and the number of interfaces within a product or product family. This relationship is very important to the product designers in terms of product innovation. In general, most innovative products have a higher number of unique components than ordinary or existing products. Unique components usually have fewer interfaces with each other than common components. In any innovative product, there are more unique components and components mixed with common components within a similar product group or product family. From equation (7), we can observe that when the number of interfaces (N ) increases within a product or module, there is less possibility of the presence of unique components (U ), which means a lesser chance of an overall innovation process. On the other hand, a smaller number of interfaces indicate the presence of a higher number of unique components, which in turn indicates the possibility of a higher innovation rate within a product or module. The contribution from this relationship between the unique component and number of interfaces is that a higher number of interfaces reduce the opportunity for developing an innovative product or module, and vice versa. 3.2 Innovation rate in terms of modularity constraint and unique component The rate of innovation within a product or module can also be presented in terms of modularity constraint (K ) and number of unique components (U ). Modularity constraint can be defined as the opposition or resistance of the modular design and development strategy. A higher number of modularity constraints represents a lesser chance of module formation, and vice versa. In other words, higher modularity constraint represents the possibility of a higher rate of product innovation. The relationship between the modularity constraint and the rate of innovation can be formulated as follows. Let us insert the value of equation (7) into equation (1) and we will get the following modularity constraint relationship as presented in equation (8) below:  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N ^ ð N Þ2 þ4 · ð N Þ ð8Þ K¼ 2 From equation (8), it can be observed that the modularity constraint (K) depends only on the number of interfaces and not on the number of unique components. However, the number of unique components depends on the number of interfaces. The relationship is presented in equation (7). Let us analyze the interrelations between innovation effort (I ) and modularity constraint (K ). From equation (2) we can observe that a higher value of K forces a firm to be less innovative. In order to intensify the innovative effort (I ), we need to reduce the value of modularity constraint (K ). Keeping this objective in mind, we have remodeled equation (2), which is formulated as follows: K dI þ I ðU Þ ¼ I max ðU Þ 1 þ K dU

ð9Þ

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The innovation difference equation (9) interprets the rate of innovation or simply innovation effort (I ) according to the change in number of unique components d(U ). Here, Imax(U ) represents the maximum point of innovation according to the number of unique components. After applying Laplace transformation to equation (9) and inverting it, we obtain the updated relationship among I, U and K as follows:   2KU ð10Þ I ðU Þ ¼ 1 2 e 1þK · I max Equation (10) interprets valuable decisive factors for an industrial establishment. From equation (10), we can observe that a higher number of modularity constraints (K ) and unique components (U ) increases the innovation effort (I ), whereas a lower value of modularity constraint and unique components reduces the possible innovation effort (I ). If we critically analyze the outcomes from both equations (7) and (10), we will obtain an important dependency pattern between the innovation effort (I ) and the number of interfaces (U ) of a component or a module. From equation (10), it can be seen that innovation effort increases with the higher amount of unique components within a product. At the same time, we can also observe from equation (7) that when the number of unique components increases within a product, the number of interfaces decreases simultaneously. It therefore can be concluded that the rate of innovation effort increases as the number of interfaces decreases; meaning higher rate of unique components within a product, and vice versa. In order to graphically represent the innovation effort or innovation level (I ) of a firm in terms of different values of modularity constraint (K) and number of unique components (U ), we have modeled equation (10). The resulting graph after the necessary simulation process is shown in Figure 1. From Figure 1, we can observe that the innovation level increases with the increasing number of unique components considering the different values of modularity constraint (K ). We can notice from Figure 1 that after a certain point, although the number of unique components increases, the innovation level reaches its maximum point. It can therefore be 0.95

Innovation level

0.85 0.75 K=1 K=5 K = 18

0.65 0.55 0.45

Figure 1. Innovation rate in terms of unique components and modularity constraint

0.35 0.25

0

2

4

6 8 10 12 14 16 The number of unique components

18

20

concluded that to achieve the maximum level of innovation, there needs to be a definite number of unique components within a module or product, which also varies from module to module or product to product. From Figure 1, we can also notice that as the value of modularity constraint (K ) increases, the innovation level (I ) also increases substantially. Thus, combining this, we can conclude from this observation that a higher number of modularity constraints, which is a result of a higher number of unique components, increase the innovation level of a product or product family, and vice versa.

Implications of interface management 13

4. Analytical model for modularity degree In design science, it is observed that there is a close relationship between the modularity degree and product innovation. The term modular innovation comes from this basic relationship, which influences the overall architecture of product design and development. Mikkola (2000) mentions that modular innovation boosts the rate of innovation, as it shrinks the time required for product design and development. Thus, modular innovation increases product variety as well as reducing manufacturing costs (Baldwin and Clark, 1997; Schaefer, 1999). It is therefore obvious that the higher the rate of modular architecture within a product or product family, the more possibility there is from an innovation perspective. This modular design and development philosophy also increases the level of manufacturing flexibility. With a view to formulating the importance of modularization in manufacturing flexibility, we develop a modularity model after considering an analogy taken from an example of controlling the water level within a tank. Let us consider a water tank as shown in Figure 2, where the water level can be compared to the modularity degree, M, and the continuous flow of incoming water can be compared with the initial number of unique components, U0. The outflow of water from the tank can be considered to be equivalent to the actual number of unique components, U1. The difference between M and U0 is considered the number of standard components S, i.e. S ¼ M 2 Uo. We have developed this modularity degree model by using time and Laplace domain dynamics, which describes the modularity degree M’s responses against a unique component change decision. These approaches describe a natural response instead of an optimal setting, even though we can guarantee their optimality by assigning the optimal modularity degree. This approach is described as follows: dM ðt Þ ¼ ð M 0 2 M ðtÞÞ;

M0 ¼ 1

ð11Þ

Equation (11) describes modularity dynamics, which is caused by the innovative components. It also shows that the modularity degree changes over time and can vary according to the level of component innovation. A higher level of component innovation U0

M

U1

Figure 2. Modularity degree change model

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triggers the rate of modularity level, and vice versa. The water model as shown in Figure 2 also supports this analogy; for instance, if the number of initial unique components U0 is increased then the level of modularity degree M will obviously be decreased, and vice versa. The same analogy applies in a water tank; that is, if the water outflow is increased, the tank level is automatically decreased. In Figure 2, inflow describes an initial number of unique components U0 and it is assumed to be constant. Outflow describes the actual level of demand for unique components (U1) and is assumed to be dynamic. The modularity decision naturally follows according to the number of unique components. Now, we consider the first order control in equation (11) in order to describe the self-regulating system, where the modularity degree M will go into a steady state at a certain level of unique component changes. Thus, the first order control system can be modeled as follows:

a1

dM þ a 0 M ¼ b · f ðU Þ dU

ð12Þ

where b · f (U ) in equation (12) is associated with the steady state condition of modularity degree after the maximum number of unique components is added. In such a case, b · f (U ) ¼ 0, as in a steady state condition the changes in a modularity degree will be equal to zero, where the maximum number of unique components is already added. Therefore, in a steady state level, equation (12) will convert into equation (13), as follows:

a1

dM þ a0 M ¼ 0 dU

ð13Þ

If we divide equation (13) by a0 and rearrange, then we develop equation (14), as follows:

a1 dM ¼ 2M a0 dU

ð14Þ

Equation (14) explains that each single unit change of the number of unique components U will change the modularity degree M as with the modularity constant a1/a0. This modularity constant is therefore considered a measure of unique components necessary for the module to adjust to a change of one unit of modularity degree. In a first order system, a1/a0 ¼ t, where t represents a reduction level in the modularity degree. The value of t gives us information on the maximum number of allowable unique components in order to enable innovation. This value also affects the reduction of the modularity degree level. Rearranging equation (13) in terms of the Laplace domain provides the following: dM a0 ¼ 2 dU M a1

ð15Þ

Equation (15) represents the rate of changes in the modularity degree with respect to the changes in the number of unique components in a product. The negative sign in equation (15) indicates that the rate of modularity degree will decrease according to the increasing number of unique components, and vice versa. Equation (15) is considered

first order modularity dynamics, where a0/a1 represents the speed of modularity degree that changes as a function of additional unique components. Similarly to Mikkola (2007), we defined a0/a1 as positively correlated to the percentage of unique components (U) over the total number of components (T ), i.e. (U/T )*100 and inversely correlated to its product substitutability s and modularity constraint K. From equation (15), we can also note that a higher number of unique components U restrict a product from being more modular, as the higher number of unique components makes it very difficult to develop the required modules needed for modular product development. The reason behind this phenomenon is that in modular product architecture, unique components create more work in terms of interface design, which is the basic requirement for attaching one component to others. This interface design consequently increases the manufacturing time. At the same time, the higher value of modularity constraint (K ) also restricts a product from being modular, as a higher number of interactions or interdependencies might be present either within a module, between modules, or between modules and their environments. In addition to the number of unique components, the modularity degree can also be interpreted in terms of component or module substitutability, s, which can be presented as the ability of a component or module to be substituted easily within many products or a product family. This substitutability factor has an influential role in determining the level of modularity degree. The component or module substitutability (s) causes a higher modularity level, because it makes the module more flexible within a product or within a product family. For instance, if a component of a given product architecture can be used in ten families (or ten times the same component), and two interfaces must be shared with other components/modules/subsystems for functionality, then the substitutability factor of the product architecture is five components per interface (Mikkola, 2007). In order to achieve the relationship of modularity degree in terms of unique components, module substitutability (s), number of interfaces (N ) and modularity constraint (K ), we convert equation (15) into equation (16), as follows: M ðU Þ ¼ e 2ðU

2

=N Þ=s · K

ð16Þ

From equation (16), we can observe that modularity degree (M ) increases by increasing the number of unique components or module substitutability (s), and vice versa. We can also formulate the modularity degree (M ) as a function of the total component number (T ), modularity constraint (K ), number of unique components (U ) and component or module substitutability (s). This is presented as follows: U2

M ¼ e 22TsK

ð17Þ

Equation (17) shows that the level of modularity degree (M ) varies exponentially on various deciding factors such as total number of components (T ), number of unique components (U ), modularity constraint (K ) and module substitutability (s). From equation (17), it can be seen that the modularity degree increases with the increasing values of modularity constraint (K ), module substitutability (s) and total number components used in a product (while the number of unique components is constant), and vice versa. We can also observe from equation (17) that when there are no unique components (i.e. U ¼ 0) in a product architecture, then the value of the modularity

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degree (M ) is equal to one, which represents a true or perfect modularization within a product or product family. In an ideal situation, the modularity degree is represented as M ¼ M0 ¼ 1, where there are no unique components (U ¼ 0). The mathematical model or formulation of the modularity degree as explained in terms of unique component, component or module substitutability, number of interfaces, and choices of modules among many options is explained through a case example in the following section. This case example is also presented in order to validate the operability and implementation scope of the modularity model as modeled in this study. 5. Modularity degree: a case example 5.1 Three module options from which to choose the best one The example presented below is basically a base for turbocharger installation. This base platform is used in particular for a large diesel engine power plant. Figure 3 shows the necessary higher level components of the base platform along with their interfaces with each other. Figure 3 also shows the component interfaces with outside environments, which might be used to connect them with other components or modules. In order to explain the modularity degree and to choose the best module option, we assume three possible module options from these basic components. All these modules are formed according to the dependency strengths between the components. In Figure 3, number “1” represents the highest dependency, while number “9” indicates the lowest dependency. Figure 4 shows the first modular option that consists of two different modules containing two and four components, respectively. Module 1, which contains components “TC support” and “bracket”, is formed according to the strong dependency strength between them. The other four components, namely “blind flange”, “hexagonal screw”, “O-ring” and “distance sleeve”, form module 2, as shown in Figure 4. Module option 2, as shown in Figure 5, also consists of two different modules containing three components each. In this case, modules 1 and 2 are formed according to the dependency strength among three components, respectively. 3 Interfaces to outside environment

Bracket

Distance Sleeve

8 6

1 9 TC support

Hexagon al screw

5

Dependency strength

2

4 5

Figure 3. Turbocharger base platform for a diesel engine

7 Component name

O-ring 3

Blind flange

Interface

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3

Module 1

Bracket

1

Distance Sleeve

6

TC Support

17

8 Hexagonal Screw

5

9

4 7

2

5

O-Ring

Figure 4. Turbocharger base platform for a diesel engine (option 1)

Module 2 Blind Flange

3

Module 1 3 Bracket

1 TC Support

9 Hexagonal Screw

5 7 Module 2

Distance Sleeve

8

6

2 4

5 3

Blind Flange

Figure 6 shows module option 3, where all six components are connected with each other to form a single module. The objective of these three module options is to find out the best module option for the base platform in order to support the turbocharger of the diesel engine power plant. 5.2 Implementation of modularity degree for the best module option We have discussed three available module options, which can be used for the installation of a turbocharger base. The objective here is to choose the best module option from three in terms of modularity degree (M) and number of unique components (U ). In order to find the best module option from the three available module options as discussed above, we have considered equation (17) for the simulation purpose.

Figure 5. Turbocharger base platform for a diesel engine (option 2)

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Distance Sleeve

8 6

1

18

9 TC support

Hexagonal screw

5

2

4 5 7

Figure 6. Turbocharger base platform for a diesel engine (option 3)

O-ring 3

Blind flange

After undertaking the required simulation process through changing the values of unique components and keeping the other values (T, s and K) constant, we obtain the result as shown in Figure 7. Figure 7 shows the values of modularity degree of the three module options. From Figure 7, we can note that the modularity degree decreases sharply as the number of unique components increases, and vice versa. For instance, in case of module option 1, the value of modularity degree is 0.99 when there are two unique components, while the value of modularity degree is 0.91 when there are six unique components. With the objective of choosing the best module option from the available three options as stated above, we have checked their corresponding values of modularity degrees with respect to the number of unique components. For example, from Figure 7 we got the values of modularity degree for module option 1, module option 2 and module option 3 as 0.92, 0.90 and 0.91, respectively, while each of the module options contains six unique components. From this observation we could conclude that module option 1 is the best option among the three as it has the highest value of modularity degree. Modularity level at increased number of unique components

Figure 7. Modularity degree reductions as a function of the number of unique components

Modularity degree

0.99 0.97 0.95 0.93 modularity option 1

0.91

modularity option 2

0.89

modularity option 3

0.87 0.85 1

2

3 4 5 Number of unique components

6

7

It is therefore concluded that a higher number of unique components within a module and/or product decreases the level of modularity. On the other hand, a lower number of unique components within a module and/or product increase the possibility to be more modular. With the proposed formulation or methodology, a designer is able to identify the best module options among several alternatives and decide on the acceptable number of unique and standard components in order to move towards a modular design from an integral one. This will enable manufacturing firms to develop successful product development strategies, thus satisfying customers’ demands comfortably. 6. Managerial implications Before implementing modular architecture in the respective product development process, managers need to thoroughly investigate their existing product development architectures, find the appropriate strategic options and choose the best one for them. Various measures, such as the total number of components, number of common components, number of unique components, number of interfaces, also influence managers in the decision-making process for a specific strategy. These technical measures are also connected to other strategic features, such as innovation possibilities within firms either incrementally or as a breakthrough, modularity degree, etc. In order to decide on either integral or modular product architecture or a combination of both, organizational managers need to investigate the possible benefits and limitations of both approaches. The superiority of modular architecture lies in its being integral with a modularization focus, which contributes to the ease of assembly, reassembly and disassembly of different products or systems. This flexibility offers more variations and ease of final products (Baldwin and Clark, 1997). This phenomenon influences the standardization and commonality of components, which are key concepts in modularization. During this research, it was noticed that the product innovation level essentially depends on the number of unique components and modularity constraints. This modularity constraint is related to the number of component interfaces, which work as a deciding factor for modular design architecture. Before implementing modular strategy, managers need to concentrate, after discussing with designers, on minimizing the number of interfaces per component as much as possible. A higher number of component interfaces reduces the level of commonality, or standardization, of components. A higher number of component interfaces creates a unique component that resists module formation or modularity. It is therefore a challenging task for industrial designers to develop such standard interfaces for components, which are the prime requirements for product modularization, but also affect innovation (Agrawal, 2009). Product innovation can be measured through the level of interfaces among components or modules. The interfaces can be standard or non-standard. In a real product design environment, it is almost impossible to generate many standard interfaces, but rather a mixture of standard and non-standard interfaces among components or modules. Standard interfaces enhance modularization, whereas unique or non-standard interfaces increase the chance of innovation, as more unique components create more non-standard interfaces, which hinder the modularization process. In this research, the number of unique components influencing the level of modularity or modularity degree was also investigated. We noted that modularity degree reduces substantially after increasing the number of unique components.

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The concept of product innovation in terms of modularity degree and number of unique components was also examined; the increasing level of product modularity which is related to the number of unique components decreases the innovation level within product architecture, and vice versa. By implementing this analytical phenomenon, managers would be able to measure the suitability of their already planned or developed modules in terms of measuring the number of unique components within a module, which will help them to choose the best module(s) for a target product. In general, organizational managers need to formulate a development strategy where there is the possibility of both modularization and innovation. In order to achieve both of these objectives, there needs to be a balance between standard and non-standard components or interfaces. A higher level of modular product architecture often restricts the innovation process, as most of the components or modules are standard or common among different products or a product family. On the other hand, uncommon or new components increase the possibilities of product innovation. In such a situation, managers need to keep a balance between standard and non-standard interfaces or standard and non-standard components/modules. This will ensure the acceptability rate of both modularization and innovation strategies, which are the key to potential success for any manufacturing firm globally. 7. Conclusions To cope with today’s business environment, manufacturing firms have recently been considering modular architecture as a competitive option that could satisfy most customers’ needs. Modular product architecture is very helpful in creating product variety together with cost benefits. However, there are limited resources for firms to find the appropriate tools or methodology in order to design their modules and to find the best alternative from several. It is therefore a research option to formulate a module based on a specific principle or strategy. In this study, we have proposed a methodology for formulating a module depending on the strength of component interdependencies. Through this approach, general dependency strengths between components are analyzed and investigated through designers’ knowledge and perceptions. The developed modules need to be compared to various available alternatives in order to prioritize their suitability as end products. In order to perform the required prioritization of specific module options, we have modeled them in terms of modularity degree, which acts as a tool or method for choosing the best module option from various alternatives. The model for modularity degree is analyzed according to the component interfacing among unique and/or standard components in general. These interfaces are the key requirements in identifying the suitability of unique components and standard components. Generally, unique components have more interfacing within a module or a product, which is not suitable for modularity but good for product innovation. On the other hand, standard or common components have a greater chance to be modular, but they restrict the innovation level. With a view to comparing the modularity degree and innovation level, a model was created in order to show the relationship between modularity degree in terms of unique components and various module options, which are taken from a case example. From the simulation output, it is observed that the modularity degree, or level, decreases substantially with the increasing number of unique components within a product or product family. This phenomenon guides product designers before the adopting of

a particular design philosophy, such as either modular or integral or a combination of both approaches. In an innovative product, there are a higher number of unique or non-standard components, which is not suitable for modular product architecture. In this case, designers need to make a trade-off between the level of innovation and modularization. The research presented in this article would be a valuable guide to product designers and manufacturing engineers in respect to interface design among components and/or modules that facilitate manufacturing/assembly of the end product. Common components have a predefined number of interfaces, while unique components do not, and vary from product to product, which reflects the architectural decisions regarding modular or integral design architecture. These research issues can be expanded by including more case products with the objective of interpreting this approach from a more practical point of view. References Agrawal, A. (2009), “Product networks, component modularity and sourcing”, Journal of Technology Management & Innovation, Vol. 4 No. 1, pp. 59-81. Baldwin, C.Y. and Clark, K.B. (1997), “Managing in an age of modularity”, Harvard Business Review, Vol. 75 No. 5, pp. 84-93. Baldwin, C.Y. and Clark, K.B. (1999), Design Rules, The MIT Press, Cambridge, MA. Baldwin, C.Y. and Clark, K.B. (2000), Design Rules, Volume 1: The Power of Modularity, The MIT Press, Cambridge, MA. Bask, A., Tinnila¨, M. and Rajahonka, M. (2010), “Matching service strategies, business models and modular business processes”, Business Process Management Journal, Vol. 16 No. 1, pp. 153-80. Chen, K.-M. and Liu, R.-J. (2005), “Interface strategies in modular product innovation”, Technovation, Vol. 25, pp. 771-82. Dahmus, J.B., Gonzalez-Zugasti, J.P. and Otto, K.N. (2001), “Modular product architecture”, Design Study, Vol. 22, pp. 409-24. Fixson, S.K. and Park, J.-K. (2007), “The power of integrality: linkages between product architecture, innovation, and industry structure”, Working Papers No. 37154, Massachusetts Institute of Technology (MIT), Sloan School of Management, Cambridge, MA. Fixson, S.K., Ro, Y. and Liker, J.K. (2005), “Modularization and outsourcing: who drives whom? A study of generational sequences in the US automotive cockpit industry”, International Journal of Automotive Technology and Management, Vol. 5 No. 2, pp. 166-83. Fujita, K. (2002), “Product variety optimization under modular architecture”, Computer-Aided Design, Vol. 34 No. 12, pp. 953-65. Garud, R. and Kumaraswamy, A. (1995), “Technological and organizational designs for realizing economies of substitution”, Strategic Management Journal, Vol. 16, pp. 93-110. Helper, S. and MacDuffie, J.P. (2000), “E-volving the auto industry: e-business effects on consumer and supplier relationships”, paper prepared for E-business and the Changing Terms of Competition: A View from W the Sectors, The Fischer Center on the Strategic Use of Information Technology, Haas School of Business, Berkeley, December. Helper, S., MacDuffie, J.P. and Sabel, C. (1999), “Pragmatic collaborations: advancing knowledge while controlling opportunism”, Industrial and Corporate Change, Vol. 9 No. 3, pp. 443-88.

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Sanchez, R. (1996), “Strategic product creation: managing new interactions of technology, markets, and organizations”, European Management Journal, Vol. 14, pp. 121-38. Sanchez, R. and Mahoney, J.T. (1996), “Modularity, flexibility, and knowledge management in product and organisation design”, Strategic Management Journal, Vol. 17, pp. 63-76. Schaefer, S. (1999), “Product design partitions with complementary components”, Journal of Economic Behavior & Organization, Vol. 38, pp. 311-30. Schilling, M.A. (2000), “Toward a general modular systems theory and its application to interfirm product modularity”, Academy of Management Review, Vol. 25 No. 2, pp. 312-34. Shamsuzzoha, A.H.M. (2010), “Modular product development for mass customization”, doctoral dissertation, University of Vaasa, Vaasa. Shamsuzzoha, A.H.M. (2011), “Modular product architecture for productivity enhancement”, Business Process Management Journal, Vol. 17 No. 1, pp. 21-41. Shamsuzzoha, A.H.M. and Helo, P.T. (2009), “Reconfiguring product development process in auto industries for mass customization”, International Journal of Productivity and Quality Management, Vol. 4 No. 4, pp. 400-17. Shamsuzzoha, A.H.M., Helo, P.T. and Kekale, T. (2010), “Application of modularity in world automotive industries: a literature analysis”, International Journal of Automotive Technology and Management, Vol. 10 No. 4, pp. 361-77. Shibata, T., Yano, M. and Kodama, F. (2005), “Empirical analysis of evolution of product architecture – Fanuc numerical controllers from 1962 to 1997”, Research Policy, Vol. 34, pp. 13-31. Sosa, M.E., Eppinger, S.D. and Rowles, C.M. (2003), “Identifying modular and integrative systems and their impact on design team interactions”, Journal of Mechanical Design, Vol. 125 No. 2, pp. 240-52. Staudenmayer, N., Tripsas, M. and Tucci, C.L. (2005), “Interfirm modularity and its implications for product development”, Journal of Product Innovation Management, Vol. 22, pp. 303-21. Ulrich, K.T. (1995), “The role of product architecture in the manufacturing firm”, Research Policy, Vol. 24, pp. 419-40. Ulrich, K.T. and Eppinger, S.D. (1995), Product Design and Development, McGraw-Hill, New York, NY. Van Hoek, R. and Weken, H. (1998), “How modular production can contribute to integration in inbound and outbound logistics”, International Journal of Logistics: Research and Applications, Vol. 1 No. 1, pp. 39-56. Wyatt, D.F., Wynn, D.C. and Clarkson, P.J. (2009), “Comparing representations for product architecture design through life-cycle evaluation methods”, Proceedings of the 2nd Nordic Conference on Product Lifecycle Management – Nordplm’09, Goteborg, 28-29 January. About the authors A.H.M. Shamsuzzoha has been working as a Researcher in the Department of Production, University of Vaasa, Finland, since April 2007. He received his PhD in Industrial Management from the University of Vaasa, Finland in 2010 and his Master of Science (Mechanical Engineering) degree from the University of Strathclyde, Glasgow, UK. Currently his research activities are devoted to the integration of business collaboration and logistics management in product development. His major research interest lies in the area of mass customization, information and innovation management, SCM and logistics. He has published several research papers both in international journals and conference proceedings. A.H.M. Shamsuzzoha is the corresponding author and can be contacted at: [email protected]

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Yohanes Kristianto obtained his DSc (Tech) in Industrial Management from the University of Vaasa, Finland. He is now working as a project researcher in the same institution. Prior to his academic career, he had experience in some industries, such as a plant engineer in a petrochemical industry, R&D supervisor in the battery industry, and quality assurance/supervision in the food industry. He also has some certifications in the Quality and Safety Assessment Series. His research interests are in the area of supply-chain strategy/management and production/operations management. Petri Helo is a Research Professor in the Logistics Systems Research Group at the University of Vaasa, Finland. He received his PhD in Production Economics from the University of Vaasa, Finland in 2001. He is also involved in developing logistics information systems at Wapice Ltd as a partner. His research addresses the management of logistics processes in supply demand networks, which take place in electronics, machine building and food industries. His areas of expertise include agile manufacturing, technology management and system dynamics. He has published several research papers in prestigious international journals and conference proceedings.

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