A Modular Product Design Approach With Key ...

Proceedings of the ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2014 August 17-20, 2014, Buffalo, New York, USA

DETC2014-35091

A Modular Product Design Approach with Key Components Consideration to Improve Sustainability

Junfeng Ma Department of Industrial and Manufacturing Engineering The Pennsylvania State University University Park, PA 16802, USA

ABSTRACT Modular product design (MPD) has attracted significant attention. The concept of sustainability has also received attention in view of the intensifying demands concerning environmental issues. Because the way a product or system is modularized can have implications on its sustainability, investigating MPD and sustainability jointly is important. To this intended joint investigation, we add the concept of a product’s key components. Mostly, product competitiveness depends strongly on a few key components; thus, we should logically design products with a concentrated consideration of their key components. However, to the best of our knowledge, only scant research addresses key components. Developing an optimal MPD method, which incorporates key components specification, is the primary motivation for this research. In this paper, we provide a sustainable modular product design approach. A graph-based clustering algorithm is offered to cluster components into modules. A module structure sustainability index (MSSI) is used to find an optimal sustainability module structure. A coffee maker case study is used to illustrate the proposed methodology. Key Words: Modular product design, sustainable product design, key components INTRODUCTION As time passes, our world becomes more and more complex. One common way to reduce complexity is through decomposition, which splits a larger system into smaller subsystems. By applying this philosophy inversely to design engineering, modular product design has evolved. Initially, this approach was proposed by Steward [1], who introduced the

Gül E. Okudan Kremer School of Engineering Design Department of Industrial and Manufacturing Engineering The Pennsylvania State University University Park, PA 16802, USA

philosophy of system partition and testing. MPD involves clustering simple and small product components into more complex subassemblies, and then combining these subassemblies to create a complete product. In modular product architecture, each functional product component is implemented in exactly one subassembly, with few interactions between subassemblies [2]. Many practical advantages of modularity have been examined in recent research. MPD has been shown to increase manufacturing efficiency [3]; it can benefit the supply chain by reducing inventory cost and saving distribution time [4-6]. It can also satisfy the demand for mass customization [7-9]. Sustainability-related research has commanded global attention, due largely to the fact that the environment and environment-related issues have increasingly become a matter of concern. Sustainable development, as the World Commission on Environment and Development (WCED) defined it more than a quarter century ago, is ―meeting present needs without compromising the ability of future generations to meet their needs‖ [10]. More recently, the US Environmental Protection Agency (EPA) described sustainability as ―the satisfaction of basic economic, social, and security needs now and in the future without undermining the natural resource base and environmental quality on which life depends‖ [11]. Sandborn and Myers modified the definition of sustainability using a more technical vantage point, stating that sustainability means keeping an existing system operational and maintaining field versions of the system such that the original requirements are satisfied [12]. Based on this definition, they classified sustainability into three broad groups: environmental, business or corporate, and technology. An additional popular

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categorization includes economic sustainability, environmental sustainability and social sustainability [13, 14], and its proponents suggest sustainability should consider these three factors simultaneously. Key components may represent core technologies for the manufacturer, such as Audi’s turbo-engine; they may also represent most costly, with most environmental impact, or most labor-hour requiring, such as the cabinet in a refrigerator. Having multiple key components in the same module may cause problems (e.g., increasing the assembly difficulty and cost during maintenance). For example, compressor and control unit in a refrigerator are expensive to manufacture and assemble. If two components are in the same module, during maintenance when one is maintained, the other also needs to be disassembled and assembled; this makes maintenance more expensive. Therefore, one good way to handle key components is separating them into different modules. Integration of MPD, sustainability and key component module assignment at the product design stage might have several positive outcomes. To the best of our knowledge, however, existing modularity methods do not address all these issues simultaneously. Accordingly, developing a modular product design method, which incorporates key components specification and sustainability, is the primary motivation for this research. Below, we present a summary of the pertinent research before discuss the method we propose. LITERATURE REVIEW MPD is a widely applied methodology in design engineering, and many MPD methods have evolved over the past several decades. Here, we summarize some of the traditional MPDs and discuss them based on their categories (i.e., matrix-function classification and Cluster-Graph-MathArtificial-Genetic (CGMAG) classifications). Zhang and Gershenson categorized MPD methods into two groups: (1) matrix-based, and (2) function-based [15]. Matrixbased methods sort product components into modules using a clustering method based on matrix characteristics. Functionbased methods require intrinsic knowledge of the complex product or system to identify functions and decompose products. We identify this category as the matrix-function classification. Among classic matrix-based MPD methods, Kusiak and Chow developed a cluster identification algorithm and a cost analysis algorithm to group components [16]. In order to increase design efficiency, Kusiak and Wang developed the triangularization algorithm based on depth-first search and applied this method along with a decoupling algorithm to group and optimize modules [17]. Huang and Kusiak later modified the triangularization algorithm by considering interaction and suitability matrices [18]. Pimmler and Eppinger applied a heuristic swapping algorithm to measure interaction among components in a module according to five different integers (-2, -1, 0, 1, 2) and

divided the interaction into four types, and put them into one entry with four numbers [19]. Similarly, Newcomb et al. defined two indexes: CR (Correspondence Ratio) and CI (Cluster Independence) to measure modularity and then applied cluster identification algorithm to re-design products [20]. Gu et al. took several design criteria into account and used integrated modular design methodology for life cycle engineering [21]. Among function-based MPD methods, Ishii et al. used a fishbone diagram to represent the relationships among modules [22]. Marshall et al. checked whether the corporate goals and product requirements were matched or not in the modular design [23]. Stone et al. used a function structure diagram to identify dominant flows, branching flows and conversion transmission flows, where each flow is a potential module or module type [24]. The matrix-based MPD methods focus on the similarities and differences among components, but these methods direct limited attention to the function-level relationships. For example, Kusiak and Chow developed two matrix-based algorithms from only the perspective of the components’ physical relationships [16]. In contrast, function-based modular design methods concentrate on functional or group relations while mostly ignoring component-level properties. For example, Marshall et al. considered only functional criteria to check the implications of modular design. Until recently, no methods have taken these two factors into account simultaneously [23]. Jose and Tollenaere’s categorization of MPD methods divides them into five groups: (1) clustering methods, (2) graph and matrix partitioning methods, (3) mathematical programming methods, (4) artificial intelligence methods, and (5) genetic algorithms and heuristics [25]. Herein, we refer to this categorization as CGMAG, using the first letter of each group. Clustering methods group components into clusters according to similarities and differences based on different design criteria [3, 16, 26]. Graph and matrix partitioning methods apply graph or matrix-based methods to sort components. Investigations of Kumar and Chandrasekharan [27] and Huang and Kusiak [18] can be incorporated under graph and matrix partitioning methods. Most matrix-based methods could be categorized into this group [19-21]. Mathematical programming methods are used to form component groups. Kusiak and Wang [17] presented a mathematical programming method, which searches for modules through the use of linear programming. Artificial intelligence, a branch of computer science, is also a useful tool for clustering components. Zhang et al. [28] discussed an evolving knowledge-based artificial intelligence technique for the modularization of components. Genetic algorithms and heuristics is a set of widely used methods to solve the optimizing problems. Kreng and Lee [29] proposed an MPD

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method that uses nonlinear programming to construct an objective function that is subject to certain constraints, and then applies a grouping genetic algorithm heuristic to search for an optimal or near-optimal modular design. A key component may represent a core technology and affect the product function performance, such as the turbocharger in an automobile engine [30], or they may be expensive or complicated to assemble or pollute the environment and consequently influence product market performance, such as the cabinet of a refrigerator [31]. However, only few papers available today address key components, with one paper partially alluding to the idea of emphasizing them during product design. Specifically, Huang and Kusiak [18] provided a decomposition approach to cluster product components. Besides interactions among components, they took designers’ preference into account in module forming. Key components could be identified and handled as per preferences. Because this method heavily relies on designer judgment, it is difficult to call it optimal or even objective. Therefore, in this paper, we propose an optimal key components-focused MPD to fill this gap. Since mathematical programming methods can consider several design criteria and make it relatively easy to form modules using computer analysis, our methodology takes advantage of mathematical programming. METHODOLOGY We combine MPD, sustainability and key components concentration in the proposed methodology. For sustainable

the manufacturing process, the maintenance process and the EOL process. We employ measures of life cycle cost (LCC), life cycle energy consumption (LCEC) and life cycle labor hour (LCLH) to evaluate modular design’s impact in terms of environmental, economic, and societal factors. In traditional manufacturing industry, labor hour determines labor employment, and accordingly determines employment rate. Therefore, in this paper, we employ life cycle labor hour to reflect how product design influences society. Since maintenance depends largely on component failure frequency, we take the failure rate into account in the maintenance process. For module forming, we adopt tri-criteria integer programming to cluster product components. The tri-criteria involve minimizing LCC, minimizing LCEC and maximizing LCLH. Then, we use MSSI index to evaluate and find the most sustainable module structure. The key components in the proposed methodology are components which are most costly (top four in LCC consideration in this paper), most energy consumption (top four in LCEC) and most labor hour required (top four in LCLH). The quantity of key components relates to quantity of product modules in that each key component should be assigned into a different module. Fig.1 shows the methodology flow chart. We analyze the product life cycle from the views of cost, energy consumption and labor hour. In the proposed methodology, we use cost only as an example to show the workings of the algorithm. As shown in Fig.2, the product life cycle is divided into three parts; therefore, the total product life cycle cost has the following corresponding parts:

Figure 1 Methodology Flow Chart design, we consider three main factors: the environment, the economy, and society. The complete product life cycle involves

manufacturing cost, maintenance cost and EOL cost. The

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classification has a significant assumption, similar to the one Chung et al. [32] discussed: the manufacturing process is limited to Original Equipment Manufacturer (OEM), which means company manufactures products or components that are purchased by another company and retailed under that purchasing company's brand name [33]. We use total cost to measure economic consideration. Eq. 1 shows this measure.

Manufacturi ng Stage

Maintenan ce Stage

EOL Stage

Life Cycle

Figure 2. Product Life Cycle

Maintenance Stage Cost The maintenance process involves removing a failed module/component and replacing it with a new one; therefore, the maintenance process includes both disassembly and assembly processes, and maintenance cost is comprised of assembly cost, disassembly cost and replaced new module/component costs. The maintenance cost is positively related to maintenance frequency [32]. Thus, maintenance frequency should be taken into account in cost calculations. Maintenance frequency depends on module/component failure rate; thus, it could be evaluated through failure rate. We use the failure rate index (FRI) to evaluate the failure rate. Therefore, the maintenance cost is calculated by Eq.3.

(1) TC = CM + CMai − CEOL where: TC means total cost in life cycle; CM means total manufacturing cost in life cycle; CMai means total maintenance cost in life cycle; CEOL means EOL stage cost in life cycle. The EOL stage cost includes two parts: total EOL option cost and module disassembly cost. Total EOL option cost could be positive or negative. Positive EOL option cost means the company can get some value back and reduce the cost, such as incineration could generate electrical power, and it could get value from selling electrical power. Negative EOL option cost means manufacture should pay money to handle the retired product/component, such as disposal.

CMai = TLC K FRI (CNC + CDA + CA ) where: CMai is total maintenance cost; TLC is product total life time; K FRI is failure rate DSM element; CNC is new component cost; CDA is the disassembly cost; CA is assembly cost.

(3)

EOL Stage Cost We use sumproduct of EOL options cost and their corresponding fuzzy weights to measure total EOL option cost. 𝐶𝑀𝐷𝐴 has to be positive because module disassembly must be paid. Therefore, the EOL stage cost is from Eq. 4. n CEOL = ∑m (4) j ∑i CEOL,ij wj − CMDA where: CEOL is total EOL cost; CEOL,ij is component i’s jth EOL option’s cost; 𝑤𝑗 is 𝑗th EOL option’s fuzzy weight; 𝐶𝑀𝐷𝐴 is module disassembly cost.

Manufacturing Stage Cost The manufacturing process involves assembling manufactured or purchased components; therefore, manufacturing cost is divided into component cost and assembly cost. For more detail, the organizational structure of manufacturing cost is shown in Fig. 3.

Tri-Criteria Integer Programming Model In a similar way to the one presented above, we can analyze energy consumption and labor hour in product life cycle. The Eq.5 shows tri-criteria integer programming model. Cost Objective: min ∑nj=1 ∑ni≠j,i=1(CC,i + Xij × CA,ij + TLC K FRI (CNC,i + Xij × CDA,ij + Xij × CA,ij ) − Xij × (CEOL,i + CEOL,j ) + (1 − Xij ) × CDA,ij ) Figure 3. Manufacturing Cost CM = CC + CA (2) where: CM is total manufacturing cost; CC is total component cost; CA is total assembly cost.

Energy Consumption Objective: min ∑nj=1 ∑ni≠j,i=1(ECC,i + Xij × ECA,ij + TLC K FRI (ECNC,i + Xij × ECDA,ij + X ij × ECA,ij ) − X ij × (ECEOL,i + ECEOL,j ) + (1 − Xij ) × ECDA,ij )

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Labor Hour Objective: max ∑nj=1 ∑ni≠j,i=1(LHC,i + Xij × LHA,ij + TLC K FRI (LHNC,i + Xij × LHDA,ij + Xij × LHA,ij ) + Xij × (LHEOL,i + LHEOL,j ) + (1 − Xij ) × LHDA,ij ) (5) where:

1, if component i belongs to component j′ s module ; 0, otherwise TLC is product total life time; K FRI is failure rate DSM element; CC,i is component i’s component cost; CNC,i is new component i’s component cost when component i fails and needs to be replaced; CDA,ij is disassembly cost when component i is disassembled from component j; CA,ij is assembly cost when component i is assembled to component j; CEOL,i is component i’s expected EOL cost; CEOL,j is component j’s expected EOL cost; ECC,i is component i’s energy consumption; ECNC,i is new component i’s energy consumption when component i fails and needs to be replaced; ECDA,ij is disassembly energy consumption of component i if component i is disassembled from component j; ECA,ij is assembly energy consumption of component i if component i is assembled to component j; ECEOL,i is component i’s expected EOL energy consumption; ECEOL,j is component j’s expected EOL energy consumption; LHC,i is component i’s labor hour; LHNC,i is component i’s portion of labor hour when component i fails and needs to be replaced; LHDA,ij is disassembly labor hour of component i if component i is disassembled from component j; LHA,ij is assembly labor hour of component i if component i is assembled to component j; LHEOL,i is component i’s expected EOL labor hour; LHEOL,j is component j’s expected EOL labor hour. Xij = {

Subject to constraints: 1) Each component is assigned to exactly one cluster ∑nj=1 Xij = 1 i; 2) Components are assigned to a predefined number of key component modules (K), one key component occupies one module ∑nj=1 Xjj = K ; 3) Components are assigned into key component modules Xij Xjj , i, j; 4) Xij = 0 or 1, i, j; 5) If Xij = 1, then component i and component j should have at least one of spatial, energy, information or materials connection.

The first, second and third equations in Eq. 5 correspond to the LCC objective, the LCEC objective and the LCLH objective. Each equation considers all three stages in the life cycle. Constraint one shows each component is assigned to only one cluster; constraint two gives the predefined cluster number; constraint three indicates that all components must be assigned into key component module; constraint four is the 0-1 integer constraint; constraint five shows components in each cluster must be connected based on at least one of connection relationships. Simplified Model Eq.5 involves twenty-one variables, and each equation includes four parts of calculation. Therefore, using Eq.5 is not easy to find a solution, even though it displays the life cycle sustainability analysis for our module structure. Thus, simplifying the model is a possible way to reduce calculation difficulty. In our analysis, we assume that the assembly sustainability values will not change as a result of any change in the module structure. Therefore, the sustainability values at the manufacturing stage also will not change. No matter how we vary the module structure, the component manufacturing sustainability values (i.e., cost, energy consumption, and labor hours) and the component interaction relationship will remain constant. Consequently, the sum of the component manufacturing sustainability values plus the components assembly sustainability values is constant, which means all sustainability values during the manufacturing stage are constant. In addition, we consider the expected sustainability values of the EOL options are constant. Hence, only the maintenance sustainability values and the module disassembly sustainability values will change if there is a change in the module structure. If we temporarily ignore the manufacturing stage values and EOL options’ expected values and emphasize the maintenance values and module disassembly values, the solution will be much easier to derive. Maintenance sustainability values can be represented by pair-wise component sustainability values through failure rate. Module disassembly sustainability values can also be represented by pair-wise component disassembly values, as shown in Fig 4.

Figure 4. Disassembly Difference between Two-Module Structures

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On the left side of the module structure, component disassembly costs DC19, DC13 and DC26 are aggregated into total module disassembly costs; while on the right side of the module structure, component disassembly costs DC12, DC46, DC56 and DC67 are included in total module disassembly cost. Hence, if we use a single component pair-wise matrix to represent maintenance sustainability value and module disassembly sustainability value, and if we use a graph search algorithm to find the optimum module structure, then this module structure will be the optimized module structure of the sustainability value. Eq.6 shows the simplified tri-criteria model.

complete link represents the distance between the farthest pair of points across two clusters. In our methodology, we define the distance between clusters as the sum of extra cluster values. Based on the discussion above, we emphasize both interaction and sustainability values. Fig.5 shows the flowchart for this key component clustering algorithm.

Cost Objective: min ∑nj=1 ∑ni≠j,i=1(TLC K FRI (CNC,i + Xij × CDA,ij + Xij × CA,ij ) + (1 − Xij ) × CDA,ij ) Energy Consumption Objective: min ∑nj=1 ∑ni≠j,i=1(TLC K FRI (ECNC,i + Xij × ECDA,ij + Xij × ECA,ij ) + (1 − Xij ) × ECDA,ij ) Labor Hour Objective: max ∑nj=1 ∑ni≠j,i=1(TLC K FRI (LHNC,i + Xij × LHDA,ij + Xij × LHA,ij ) + (1 − Xij ) × LHDA,ij ) (6) Graph Clustering Algorithm with Interaction and Sustainability Concern We use a graph-based clustering algorithm to group components into modules. We optimize the sustainability values with concern for interaction. Before providing an algorithm flowchart, we discuss the physical interactions among components. We divide component interactions into two types: direct connection and indirect connection. Direct connection means two or more components interact directly, having a common interface or sharing a common edge. Indirect connection means two components do not interact directly but that there is common component between them; this means their interaction depends on the third component. Based on these two types of interactions, we propose a product module connection statement: All product components in one module must be connected with each other via direct or indirect connection. The theorem posits that components in a module must connect with each other and that it is impossible for one component to exist alone in a module. Therefore, if a component interacts with only one other component, these two components must be grouped in the same module. The definition of distance between clusters (modules) addresses three types of distances: single link, average link and complete link. Single link distance between two clusters is the distance between the closest points of each cluster; average link represents the distance between the cluster centroids; and

Figure 5. Key Component Clustering Algorithm Flowchart The algorithm is suitable for sustainability value, cost and energy consumption. For labor hours, however, a slight revision is needed. The measured objective of considering labor hours suggests using a maximum; therefore, the clustering strategy should involve grouping into maximal distance key component modules. Module Structure Sustainability Index (MSSI) When we got the optimal module structure value for cost, energy consumption and labor hour, we use them to form the Module Structure Sustainability Index equation, and evaluate all possible module structures to find the most sustainable one.

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The possible model structure represents key component module structure under the consideration of LCC, LCEC and LCLH. For each of these considerations, since key components may not be the same, module structure may not be the same either. We need to evaluate all these module structures. The MSSI index is shown in Eq.7. Zi = α

LCC LCC

+β

LCEC LCEC

+γ

LCLH LCLH

(7)

where: α, β, γ are weights and determined by customers; i refers to ith module structure.

Figure 7. Coffee Maker Cost Assembly DSM Attributes (Adopted from [35])

CASE STUDY We use coffee maker as a case study to show how to implement the proposed methodology. The case study only covers the implementation of graph clustering algorithm in terms of minimizing cost and does not take the key components identification into account in order to compare it to existing methods. Therefore, the result only shows the module structure with minimum life cycle cost, which is corresponding to LCCmin in Eq.7. Fig.6 shows a real coffee maker and Table 1 shows the components’ attributes. Fig.7 and Fig.8 show the assembly and disassembly cost information.

Figure 8. Coffee Maker Cost Disassembly DSM Attributes (Adopted from [35]) In order to compare with decomposition approach [18] and ASCEM [35], we assume components 5,6,7 and 8 to be key components. We apply the graph clustering algorithm to coffee maker; the comparison results are shown in Table 2. Table 2. Comparison between DA, ASCEM and Proposed Method

Figure 6. Coffee Maker (Adopted from [34]) Table 1. Coffee Maker Components' Attributes Attributes (Adopted from [35])

In Table 2, life cycle cost is sum of manufacturing stage cost, maintenance stage cost and EOL stage cost. Manufacturing stage cost is sum of components cost and assembly cost. Since we assume there is no maintenance stage for coffee maker, maintenance stage cost is 0. EOL option cost and module disassembly cost form EOL stage cost. For EOL option cost, we get the result from our current working paper [36]. In order to compare three algorithms and identify the

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improvement of proposed algorithm, we set component 5,6,7,8 as key components for all three algorithms. The module disassembly costs by DA, ASCEM and proposed algorithm are $0.46, $0.43 and $0.36, separately. From Table 2, DA focuses on physical interaction and design preference, while ASCEM emphasizes physical interaction and supply chain cost (in the module structure). The proposed methodology highlights sustainability (cost in this case), and considers physical interactions. The cluster algorithm in DA is a triangulation algorithm, which is not an optimal approach; ASCEM applies a heuristic iteration algorithm to evaluate all possible module structures and search for an optimal module structure. The proposed method here applies a graph cluster algorithm, which finds the optimum using a search heuristic. DA partially specifies key components by using design preference. For example, component 5, 6, 7, 8 are set strongly un-designed in suitability matrix in order to handle as key components separately, which means these four components must be grouped into different modules. The identification of key components depends on designer preferences presented in the suitability matrix; these preferences are subjective. The method proposed here identifies key components not only as per designer preferences, but also by comparing components, such as selecting most costly component as the key component; therefore, it is more objective. ASCEM could not identify key components. In addition, recent research shows that DA has several limitations in clustering components [37].

CONCLUSIONS AND FUTURE WORK The proposed modular product design methodology specifies key components, and take them into account in the design stage firstly. The proposed methodology also provides an optimal sustainable module structure which covers three aspects of sustainability. However, we did not consider how the product module structure affects assembly process and assembly sustainability values and modify the cost module structure; we have to put this concern in the future work. In addition, coffee maker has no maintenance stage, to verify our model more precisely; we will apply our methodology into other case studies, such as a refrigerator.

ACKNOWLEDGEMENT This manuscript was written while the author Gül E. Okudan Kremer was serving at the NSF, and includes NSF support through her Independent Research and Development plan.

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