Computer-Aided Generation of Modular Designs ...

Proceedings of the ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2012 August 12-15, 2012, Chicago, IL, USA

DETC2012-71180 Computer-aided Generation of Modular Designs Considering Component End-of-Life Options: Implications for the Supply Chain a

a,b

Nirup Philip and Gül E. Okudan a Department of Industrial and Manufacturing Engineering b School of Engineering Design The Pennsylvania State University University Park, Pennsylvania 16802

Karl R. Haapala School of Mechanical, Industrial, and Manufacturing Engineering Oregon State University Corvallis, Oregon 97333

ABSTRACT Given the growing demand for product customization, modularization is a viable way to reduce the complexity of new product development. This study presents a framework to incorporate component end-of-life options through modularization during the early design stages, to simultaneously account for supply chain factors as well as evaluating design variants. In order to accomplish this, we extend an existing software framework; this software is aimed at creating a computational design tool to aid designers in developing new modular products, by taking into account design for assembly (DfA) and design for variety (DfV). We present an extension to that work where the user has the ability to generate modular designs considering component end-of-life options, and to optimize relevant supplier selections, either to minimize costs or carbon footprint. We compare the results of this modularization with the widely used decomposition approach (DA). Overall, this computational tool enables users to understand the trade-offs between product design and supply chain performance, and the presented investigation on the two modularization methods (DA and Green DA) attests to the implications of design decisions throughout the supply chain and across the product life cycle.

Kyoung-yun Kim Department of Industrial and Systems Engineering Wayne State University Detroit, Michigan 48202

[19-22]. Modularity is frequently used as a means to support DfV. Modules can be building blocks of a product architecture. A module can be simply defined as the prescribed grouping of a selected number of components in a product to support a purpose, such as design for quick assembly or maintenance. Modularity also refers to the degree to which there can be mixing and matching of components in the product architecture; prescribed grouping of components requires component groupbased interfaces, which in turn allows modules to be swapped. This enables ease of manufacture and assembly. Through replacement of functional components, a modular architecture enables creation of new product variants with little or no time. The life cycle of a product starts with the initial product specification and ends with the withdrawal of the product from the marketplace, or abandonment [27]. Typically, abandonment starts happening when the products sales start declining as illustrated in Figure 1. It is important that the individual components are dealt with carefully when the product retires. There could be some components that are still usable and some others may require recycling. Recycling requires the separation and classification of materials by their types and properties. Modular architecture can enable separation and sorting of these materials for proper recycling or disposal. Further, individual modules can be designed so that components with similar endof-life options can be grouped; studies have indicated that at the retirement stage of a product some of its modules might be reusable or remanufacturable [11-12].

1. INTRODUCTION Mass customization and global competition have led to a shift from single product design to the design of product families [25]. Hence, product variety management has become important in paving the way for the concept of design for variety (DfV)

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physical components [30]. Product architecture can be modular and integral. Modular architecture is when there is a one-to-one mapping from the functional elements to the physical components, whereas in the integral architecture there is a complex mapping from physical to functional components. At the conceptual design stage, the physical and the functional nature of the design has to be determined. Researchers have focused on the functional basis of design to generate conceptual models (e.g., [28]). Many methodologies exist to generate modular design concepts at the detailed design stage; however, few methodologies are reported in the literature dealing with modularity at the conceptual stage [13]. Modular product development has started gaining popularity due to its potential for reducing development time and cost, while providing variety [30]. One of the most important steps when designing the architecture of a product is determining the number of modules that go into the product. Ideally, each module satisfies a specific function, enabling interchangeability. This, however, cannot happen in every case because there always will be a certain amount of interaction between different components. Hence, the interaction between different modules needs to be defined. Module development enables concurrent engineering because it decouples the product into different sub-tasks to shorten product development time [30]. Product architecture performance from the life cycle perspective can be used as a coordination mechanism for decision-making across product, manufacturing process, and supply chain domains. For example, a 3-dimensional concurrent engineering approach is developed that enables product architecture assessments by building on characteristics such as component commonality, product platforms, and product modularity, focusing on the Functional Component Allocation (FCA) of the product architecture [10]. The FCA is mapped in three steps. In the first step, a matrix that contained the different product functions and the components associated with those functions was constructed. In the next step, two indices are calculated; the first determines the number of components that jointly contribute to a function, and the second determines the extent to which these components contribute to other functions. The third step involves mapping of the different functions into a function-component allocation map. The interface between different modules plays an important role because it determines the types of interactions that exist between different modules. The four generic interactions that can exist between different components in product architecture are as follows: spatial, energy, information, and material [24]. Reversibility of the interface takes into account the difficulty of physically separating the different components and also the position of the interface in the overall product architecture. Interface standardization enables easy component substitution, and reversibility makes the reversal of the operational sequences of the supply chain possible [10].

Figure 1. Product Life Cycle Period and Revenue [18] Successfully steering the product development for maximum life cycle benefits also requires managing the product supply chain carefully. The supply chain council defines a supply chain as “every effort involved in producing and delivering a final product or service, from the supplier’s supplier to the customer’s customer” [29]. Supply chain management aims at meeting the customer’s demand more efficiently. It strives for making the right product, for the right customer, in the right amount, and at the right time [11]. As supplier contracts span over a long period of time across the product life cycle, they contribute substantially to supply chain sustainability performance, and thus, suppliers should be selected carefully accounting for not only the cost implications but also the environmental and social impacts. With growing environmental concerns and increasing supply chain costs and the need for variety in products being offered, a balance between the product architecture, component/module end-of-life considerations and supply chain management has to be struck. As such, this research work focuses on developing a software tool that enables users to understand the trade-offs between sustainable design and supply chain performance. As part of this software development, it is critical to understand the implications of the modularity methods adopted as the impact of modularity on supply chain performance has been shown [9]; thus, in this paper we compare two modularity methods (i.e., the Decomposition Approach (DA), and a modified DA, called Green DA). The following sections of the paper first provide a summary of the relevant literature, and then proceed to introduce the proposed approach, followed by the results and conclusions. 2. LITERATURE REVIEW We provide summaries of relevant prior work in the following sections concerning product architecture, supplier selection, and sustainability. 2.1 Product Architecture Product architecture is defined as the scheme by which the function of a product is allocated to physical components [30]. Product architecture consists of the arrangement of functional elements, the mapping from functional elements to physical components, and the specification of the interfaces among the

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2.2 Supplier Selection Traditionally, supply chain decisions were made after the product architecture was fixed. However, due to potential downstream complications, such as increased cost and lack of appropriate suppliers, there is a growing interest to make supply chain decisions at the product development stage. Integration of suppliers into the product development process lets companies gain more access to better information earlier in the product development process [23]. Information exchange on jointly agreed upon technical specifications can also improve the overall capability of the product. Supplier selection methods were classified into two categories as appraisal methods and mathematical methods (see Figure 2) [1]. Appraisal methods compare suppliers based on criteria ranking or cost to evaluate their performance. Mathematical methods help evaluate trade-offs among selection criteria by linear weighting, optimization, statistical and neural network techniques [1]. A combination of the two methods leads to the development of the hybrid methods. The primary goal of measuring supply chain performance is to determine the optimal component, and its supplier, that can be used in developing the product. Different factors such as cost, quality, flexibility, responsiveness, and trust are taken into consideration.

for the target market to be made allowing platform based development of products and also eliminating the need to develop products specific to the target market. By using postponement strategies certain elements of the supply chain can be performed after the customer orders are received [17]. This strategy, however, requires the product to be appropriate designed so that postponement is possible; modularity is frequently used in such applications. 2.3 Sustainability A 1987 United Nations conference defined sustainable development as that which will “meet present needs without compromising the ability of future generations to meet their needs" [32]. The objective of sustainable development is the creation of a product, a system, or a process that satisfies the functional requirements for a particular desired level, while at the same time producing low-impact or no-impact on the environment [6]. Consideration of sustainability within product development requires assigning an end-of-life option to designed components, modules and products. These end-of-life options are referred to here as reuse, recycle, and dispose. After the retirement of the product, working components are often refurbished and resold domestically or abroad. Depending on its characteristics (material, geometry, etc.), a component can be reused or recycled. Some old components are often sold at cheaper prices to developing countries [18]. Reusable components are those that can be remanufactured. Recycling typically entails the recovery of the product by removing hazardous components followed by a shredding of the recovered materials [18]. Components that cannot be recycled or reused are disposed. By grouping all the disposable components into one module, one can separate it completely from the rest of the product at end of life.

Supplier Selection Methods

Hybrid methods

Mathematical Methods

Appraisal Methods

Criteria Ranking

Cost

Supplier Performance Evaluation Methodology

Optimization

Linear Wrighting

Statistical

Neutral Network

3. PROPOSED METHODOLOGY In order to strike the necessary balance of correctly modularizing the product to get the maximum benefit for its entire life cycle, we adopt the philosophy of simultaneously designing the product architecture and its supply chain. Doing this simultaneous optimization requires a careful look at the potential matching between product architecture variants as well as intended performance of the supply chain (e.g., reducing costs and/or minimizing environmental impact). A critical piece at this juncture is what to use as a modularization method; thus, as indicated earlier, we compare DA and a modified version of it (Green DA) for their cost and carbon footprint implications across the supply chain. The overall goal is to understand the potential benefits of simultaneously optimizing the product architecture and supply chain considerations during the early design stages, while making the most appropriate modularity decisions. As per this stated goal, the software platform originally developed by Gupta and Okudan [14] needed to be extended not only to include supply chain optimization but to serve as an

Supplier Criteria Tradeoffs

Figure 2. Supplier Selection Methods [1] Three major steps were deemed necessarily in order to combine product development with supply chain: (1) a detailed assessment of suppliers involved, (2) the inputs that suppliers were willing to share, and (3) supplier input and involvement in the assessment of cost, schedule, and other business factors critical to the new product development project [23]. Consequently, some have initiated design for supply chain (DfSC) as a focus. One of the most commonly used DfSC strategies is postponement. Hewitt-Packard has proved that postponement strategy can benefit it in inventory cost reductions [17]. This idea was illustrated with the example of Rainbow, which was the code name of a new product, a computer peripheral device. Postponement strategies allow product differentiation at the final step of design and, thereby, allow the changes necessary

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functions and flows were obtained from a standard vocabulary set which is referred to as functional basis [28]. The function to be performed is mentioned inside the boundary of the box, while input energies, materials and signals flows are identified to the left of the box [7]. The output energy, material and signal flows are indicated to the right of the box, which identify the remainder flows obtained after the overall function has been executed [7]. The EMS diagram for the bicycle case study that is developed for this research is shown in Figure 4.

alternative way of modularizing the product architecture. Overall flow of the intended use is provided in Figure 3. Start Enter Functional Requirements (EMS diagram)

Obtain components from the design repository by DFA index

Select components that are needed for modularization

Design Repository (MySQL Database)

3.2 Design Repository Design repositories enable recording and reusing best design concepts/practices when required later on. Design knowledge can be stored and retrieved whenever required. “A design repository is defined as a heterogeneous product design database in which various design solutions can be searched and reused” [5]. In Computer Aided Design (CAD) tools, design repositories are widely being used to retrieve good designs whenever needed.

Framework developed by Chiu et al.

Enter the suitability Matrix

Suitability Matrix (Based on PreDefined Rules)

Modularized Design using the regular DA

DA with the generated Suitability Matrix

Regular Model

Model taking into account End of Life options

Generate DFV index

Generate DFV index

Generate Supply Chain Costs

Generate Supply Chain Costs

3.3 DFA Index and Decomposition Approach Thirteen different criteria pertaining to the components (e.g., weight, number of unique components, stiffness, length, presence of base component, etc.) were evaluated for a particular design to yield a DfA index [15-26]. A matrix based methodology called as Decomposition Approach (DA) [16] is then used to modularize the design. Two matrices used in this method are: an interaction matrix and a suitability matrix. The interaction matrix represents the interaction between the different components, and the suitability matrix represents the suitability of inclusion of the components in each of the modules. Figure 5 shows the suitability matrix, where the suitability level is represented using letters. In this representation, “a” means strongly desired, “e” means desired, “o” indicates strongly undesired, and “u” means undesired.

Select appropriate model

Stop

Figure 3. Overview of the Proposed Methodology One significant difference from prior work is that, in this study, we not only allow for simultaneous design of the product architecture and its supply chain, but also explore the impact of grouping components with similar end-of-line options together, or not. Figure 3 presents an overview of the methodology. The proposed software flow changes were implemented, as was done before in [14], using JAVA SWING within the NETBEANS IDE 6.1 programming environment. MySQL database is used for storing all the various database tables within the design repository and Java database connectivity (JDBC) is used to open MySQL tables within the Java environment. Critical aspects of the proposed flow are the use of energymaterial-signal (EMS) functional model, a design repository, a DfA concept filtering algorithm to complete a preliminary concept selection, green DA as a modularity mechanism and the calculation of supply chain costs. These aspects are explained below. 3.1 Functional Requirements EMS model is created by decomposing the overall function of the product into simpler sub-function or flows. These sub-

Figure 5. Suitability Matrix

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Saddle Human

Import

Human

Assemble

H.E.

Break E.E.

Store

E.E.

Human

Position

H.E.

H.E.

Transmission

Human

Convert

Supply

Human

Human

Stabilize

H.E.

H.E.

Regulate

Human

T.E.

Convert

Visual Signal M.E.

On/off Signal E.E.

E.E.

Orient

H.E.

M.E.

H.E.

Stop

Human

Convert

Transfer Actuate Battery + Motor (Optional)

H.E.

H.E.

Transport

Support

H.E.

R.E.

Actuate

Fork

Frame

E.E.

Convert

T.E.

H.E.

Dis-Assemble

Export Human

Human

Wheel

Human

Human Body (Human) Human Body (Human) Signal

Figure 4. EMS Diagram Adopted from [8] 3.5 DFV Index A DFV index is an indicator of the amount of redesign required for each component within a product in order to meet the future market requirements [21]. A two phase QFD technique is adapted, in the first QFD phase; a relationship is developed between the customer needs and the engineering metrics (EM) [21]. Engineering needs are measurable items, which are translations of customer needs into engineering specifications [14]. The next step involves estimating the range of change of the different customer needs. 3.6 Supply Chain Costs Supply chain costs include component costs, transportation costs and assembly costs. For the bicycle case study, transportation cost is substantially high because suppliers are located in different parts of the world. In this study, transportation costs and component costs for a single component or module is calculated to be one of the major criteria to select suppliers. Supplier options and their relevant cost information have been determined with significant input from our industrial partners as well as several logistics sites.

Figure 6. Interaction Matrix Figure 6 shows the interaction matrix specific to the bicycle case study. The suitability matrix is generated based on designer judgment. 3.4 Green Decomposition Approach In the green decomposition approach (Green DA), a modification of the regular DA is considered, where the suitability matrix is generated based on the component end-oflife options. The same algorithm for the decomposition approach is used except that this time the suitability matrix is automatically generated based on the end-of-life options as was done in [18]. Table 1 shows the suitability matrix mapping based on end-of-life options.

4. CASE STUDY We have implemented the proposed approach using a realistic bicycle case study. The primary purpose of a bicycle is to provide transportation. Although there are various types of bicycles based on function and usage (e.g., road bikes, mountain bikes, etc.), for this study, the city and path bike is chosen. Figure 7 shows the simplified architecture of a bike and its supply chain structure.

Table.1 Suitability Matrix based on End-of-life Options Reuse Recycle Disposal

Reuse

Recycle

Disposal

Strongly desired Desired

Desired

Strongly undesired Undesired

Strongly undesired

Strongly desired Undesired

4.1 Bicycle Product Structure and Supply Chain Structure The architecture of a bike and its supply chain structure can be simplified as shown in Figure 8. The components of the first level are structure, braking system, transmission system, and wheel system. Structure is composed of three sub-structures: fork, frame and saddle. The braking system, as in its name, is responsible for decelerating the bike speed. Another important

Strongly desired

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sub-system is transmission, which serves as one of the key functions that translates human power to rotational energy in the cycling process. Wheel system enables the bike to move by creating friction with the ground. These four sub-systems are mutually independent but cooperate as a whole product. Two other sub-systems are the electric motor with battery set and accessories, which are optional equipment, and thus are not included in this case.

Tier II Suppliers

Tier I Suppliers

Distributors

Customers

Metal (Alloy) Pipe

Consumers/End-Customer

Mass market

Structure System Foam Brake System Plastic Wheel System

Fork

Bike Assembly

Independent Bike Distributor

Rubber

Structure System

Frame

Braking System

Saddle

Transmission System Metals

Bike

Sports Store Paint

Transmission System

Motor & Accessory

Figure 8. Bike Supply Chain Structure Adopted from [8].

Wheel System

Table 2. Estimated Cost and Time of Processes [8]

Motor Accessory

Figure 7. Simplified Bike Architecture. Adopted from [8]. The EMS model (shown in Figure 4) considers a total of seven components and functions. The EMS model allows unbiased selection of the product components to be used in the following methodology phases. First, the functional requirements of a product are defined and decomposed into the most basic sub-functions to form an EMS functional model. Second, a repository is utilized to synthesize potential components of all sub-functions, providing multiple options for the conceptual design. These concepts are evaluated using a DfA index and then modularized with the decomposition approach (DA) or the Green DA approaches. It should be noted that in order to reduce potential bias separate team members completed the analysis to decide the end-of-life options, and the overall CF calculation. Upon running the software, the supply chain costs and optimum suppliers can be viewed for both the designs. The concept, which best suits the designer needs, can be chosen as the final design. Possible bike suppliers were surveyed worldwide [2-4] and 12 suppliers were selected for this case study. Table 2 shows the list of component and module suppliers with their cost and time to manufacture, in normalized values. Assembly cost is mentioned for the module suppliers. As the total number of components and the relevant suppliers are not too prohibitive, the optimum choices are found through search loops within the software after enumeration of all combinations. We have also developed mathematical programs to handle a much more complicated data set (see [8] for further information on mathematical programming implemented).

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Supplier (A) Saddle 2-Hip (USA West) BBB (Holland) ATOM Lab (USA West) Velo (Taiwan)

Cost

Time

9 8 6 6

6.5 6 5 3.5

Supplier (B) Frame X-bike (USA East) 2-Hip (USA West) Bombshell (USA West) ATOM Lab (USA West) Axxis (USA West)

Cost

Time

12 10 11 13 10

18 18 26 17 21

(C) Fork X-bike (USA East) Bombshell (USA West) SRAM (USA West) Spinner (Taiwan)

Cost 10 11 14 16

Time 10.8 8.8 7.5 6

(D) Brake BBB (Holland) ATOM Lab (USA West) SRAM (USA West) Shimano (Japan) Falcon (Taiwan)

Cost 5 5.2 6 6.6 6.8

Time 3 2.6 4.8 2.5 2.2

(E) Wheel BBB (Holland) Bombshell (USA West) ATOM Lab (USA West) Shimano (Japan) ALEX (Taiwan)

Cost 9 8 8.5 8.5 8

Time 11.4 12.2 10.4 10.6 9.8

(F) Transmission BBB (Holland) Bombshell (USA West) ATOM Lab (USA West) SRAM (USA West) Shimano (Japan) Falcon (Taiwan)

Cost 8 10 12 14 15 13

Time 5.5 5 4.5 4 3.5 3

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(AB) Module 2-Hip (USA West) ATOM Lab (USA West)

Cost 5 8

Time 2 1.7

CD (Module) Bombshell (USA West) SRAM (USA West)

Cost 9 7

Time 0.4 0.5

EF (Module) BBB (Holland) Bombshell (USA West) ATOM Lab (USA West) Shimano (Japan)

Cost 3 4 5 6

Time 2.5 2.3 2.1 2

Supplier (DF) Module BBB (Holland) ATOM Lab (USA West)

Cost

Time

9 12

3 3.5

Supplier (ABCE) Module Bombshell (USA West) Shimano (Japan)

Cost

Time

7 18

12 15

Supplier (ABCDEF) Module X-Bike (USA East)

Cost

Time

10

2

Table 3. Transportation Cost of Bike Components. Adopted from [8]

Components Saddle Frame Fork Brake Wheel Transmission

Freight Class 70 (fabrics) 60 (steel pipe) 60 (steel pipe) 70 (tools-non-electric) 60 (steel pipe) 85 (transmission)

Sea Shipping Cost (USD) 0.1 3.95 0.13 0.06 1.34 0.11

The transportation times between different locations were determined through logistics websites [33-35]. Table 3 shows the transportation cost, and Table 4 shows the transportation costs. The total transportation cost is the product of the number of days taken for transport with the transportation cost based on the freight class [8]. The time taken for transportation from one location to another is shown in Table 4.

CA ->PA (USD) 0.293 0.402 0.974 1.163 2.066 0.689

IL->PA (USD) 0.139 0.187 0.476 0.584 1.007 0.319

NY ->PA (USD) 0.218 0.932 0.457 0.546 0.934 0.357

information is used. The module carbon footprint is the sum of the individual components carbon footprints along with the transportation and assembly relevant CF data. Thus the total carbon footprint of the product will depend on the components selected. The comparisons of results are given in Tables 5 and 6, and the results obtained using green DA and regular DA from the software is shown in Figure 10.

4.2. Results and Discussion Product architectures were generated based on the regular decomposition approach (DA) and the green decomposition approach. For DA, the suitability matrix given in Figure 4 was used. The product architecture generated using DA is shown in Figure 9(a). A three-module product architecture was generated with two components in each of these three modules. Figure 9(b) shows the product architecture generated when the green DA was used. A two-module architecture was generated with four components in the first module and two components in the second module. The DfV indices can be calculated for both of these modular architectures. Using the software, we have compiled the optimum set of suppliers for both of these structures, and found that their supply chain costs are comparable. Figure 10(a) and (b) show the product architecture augmented with the selected suppliers. The carbon footprint (CF) for the components present in the design repository is calculated using SimaPro, and LCA software. For the carbon footprint calculations, material, manufacturing process, supplier location and transportation

Figure 9(a). Modules Obtained Using DA Note that in Table 5, the results shown reflect the minimized cost and CF conditions without the consideration of relevant transportation data (neither for cost nor for CF). On the other hand, in Table 6, results do include the transportation implications. The results show that with the inclusion of carbon footprint as a factor to select suppliers there is minimal increase in the overall cost. It is also seen that with the green DA as the modularity

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method, for this case, designs with smaller CF values are achieved, for both cost minimization and the CF minimization case.

Figure 9(b). Results obtained after Green DA Table 4. Transportation Time between Locations [30] Taiwan Japan Holland USA East 1 5 35 40 5 1 40 35 35 40 1 30 40 35 30 1 25 25 40 7

Area Taiwan Japan Holland USA East USA West

USA West 25 25 40 6 3 ATOM LAB Saddle (A)

ATOM LAB Saddle (A)

2-HIP

2-HIP

Structure Module

Frame (B)

Bombshell Structure Module+ Orientation Module

2-HIP Frame (B)

X-Bike Fork (C)

X-Bike X-Bike

Bike

SRAM

Fork (C)

X-Bike

Orientation Module BBB Brake (D)

BBB

Bombshell Wheel (E)

Bike

BBB Wheel (E)

Brake (D)

Transport Module

Transport Module Brake (F)

Transmission (F)

Figure 10. (a) Product Architecture and Suppliers for DA b) Product Architecture and Suppliers for Green DA In the second case, where transportation information is included, the results show that transportation plays a vital role in determining the carbon footprint and overall cost. In the green DA approach for both cases components are combined in such a way that disassembly is easy; in other words, components with the same end-of-life options are combined together. Thus the carbon footprint reduces because after retirement of the product individual components do not need to be removed from the module before sending to an end-of-life processing facility. The entire module can be either recycled or disposed. However, CF due to the transportation is considerable. For this case, Green DA approach does not produce the better CF results in comparison to DA, and in fact, produces the opposite result to that of in Table 5. This situation points to the need for developing modularity methods that can consider both component end-of-life options as well as the overall CF minimization.

5. CONCLUSIONS In this paper, we presented a software platform that can automatically create modular architecture using either the decomposition approach, or a modification of it that considers the component end-of-life options (Green DA). Our results indicate that when transportation relevant cost and CF data are not included in the comparisons, Green DA performs better in terms of total product cost and CF. On the contrary, when the transportation data is included, DA performs better on the same performance measures. These results point to the need for development of modularity methods that take into account both the component end-of-life options and total supply chain CF of a product.

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Table 5. Transportation Cost & Carbon Footprint Not Included 3-Module Assembly (Regular DA) 2- Module Assembly (Green DA) No Transportation Carbon Footprint (kg CO2 eq.) Total Cost Number of Suppliers

Carbon Footprint Minimization 30.96 78.10 6

Component Cost Minimization 31.30 72.00 6

Carbon Footprint Minimization 29.84 79.10 5

Component Cost Minimization 30.18 73.00 5

Module and Component Information ABCDEF ABCE AB CD EF DF A B C D E F

X-Bike

X-Bike

2-Hip SRAM BBB

2-Hip SRAM BBB

BBB 2-Hip X-bike Shimano Shimano Bombshell

ATOM Lab 2-Hip X-bike BBB Bombshell BBB

Bombshell

Bombshell

BBB BBB 2-Hip X-bike Shimano Shimano Bombshell

BBB ATOM Lab 2-Hip X-bike BBB Bombshell BBB

Table 6. With Transportation Cost & Carbon Footprint Included 3-module Assembly (Regular DA) 2- Module Assembly (Green DA) Carbon With Transportation Supply Chain Cost Carbon Footprint Supply Chain Cost Footprint Minimization Minimization Minimization Minimization Carbon Footprint (kg CO2 eq.) 31.60 31.85 32.46 32.85 Component Cost 78.10 77.00 82.10 70.00 Transportation Cost 10.20 4.74 10.33 5.82 Total Cost 88.30 81.74 92.43 75.82 Number of Suppliers 6 6 7 5 ABCDEF ABCE AB CD EF DF A B C D E F

X-Bike 2-Hip SRAM BBB BBB 2-Hip X-bike Shimano Shimano Bombshell

Module and Component Information X-Bike X-Bike Bombshell 2-Hip SRAM BBB Atom Lab ATOM Lab BBB 2-Hip 2-Hip X-bike SRAM BBB SRAM BBB Shimano BBB BBB

X-Bike Bombshell

BBB ATOM Lab 2-Hip X-bike BBB Bombshell BBB

gratefully acknowledge the contributions from Mr. Saraj Gupta and Dr. Ming-Chuan Chiu. Without their knowledge and help to share information, the analyses presented would not be possible.

ACKNOWLEDGEMENTS This work is funded by the National Science Foundation under grant numbers OCI-1041423 (Oregon State), OCI-1041328 (Penn State), and OCI-1041380 (Wayne State). We also

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