An innovation funnel process for set-based

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An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation a

A. Jeang & Francois Liang

b

a

Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan, ROC b

Department of Research and Development, Cycling & Health Technology, Industry R&D Centre, Taichung, Taiwan, ROC Available online: 09 Jan 2012

To cite this article: A. Jeang & Francois Liang (2012): An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation, International Journal of Production Research, DOI:10.1080/00207543.2011.625050 To link to this article: http://dx.doi.org/10.1080/00207543.2011.625050

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International Journal of Production Research 2012, 1–19, iFirst

An innovation funnel process for set-based conceptual design via DOE exploration, DEA selection and computer simulation A. Jeanga* and Francois Liangb a Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan, ROC; bDepartment of Research and Development, Cycling & Health Technology, Industry R&D Centre, Taichung, Taiwan, ROC

Downloaded by [Feng Chia University] at 22:41 09 January 2012

(Received 28 January 2011; final version received 7 September 2011) This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage. Keywords: conceptual design; set-base design; DOE exploration; DEA selection; ANSYS

1. Introduction New products often provide an opportunity for a firm to achieve success and earn extra revenue. The reason is that customers tend to prefer the most recent products on the market; thus, the request for cycle time reduction to improve time-to-market is a critical issue. Design decisions made early in the design stage account for a very small amount in terms of the overall product cost, but they have a major impact on the product cost. Similarly, product quality cannot be ensured unless it is built into the product before manufacturing. Hence, the operation of new product development (NPD) is an important part of a firm’s survival strategy in a competitive environment. Because the success of a product, especially an innovative product, highly depends on the efficiency and effectiveness of the NPD process, there is a need to produce products at a lower cost with higher quality, superior performance and a short product development cycle. This demand has driven us to build a systematic and competent approach for NPD. Conventional approaches, such as statistical methods or optimisation techniques, provide a robust way for engineers to determine the optimal parameters during the process of new product development (Bendsoe and Kikuchi 1988, Jeang 2008, Jeang et al. 2008, Del Prete et al. 2010). However, these tasks occur at a late stage of the product development cycle. The cycle focuses on a detailed design for optimisation or robustness under a given design alternative. Thus, a decision-making process for a specific design alternative will generally end with a local design optimisation. The reason designers are unable to find a global design optimisation for this type of design process is that inappropriate alternatives are selected from a set of poor representative alternatives for design optimisation. For example, early in the product development cycle, designers often exclude some alternatives and conduct design optimisation for a specific design before enough viable alternatives are determined. This problem

*Corresponding author. Email: [email protected] ISSN 0020–7543 print/ISSN 1366–588X online ß 2012 Taylor & Francis http://dx.doi.org/10.1080/00207543.2011.625050 http://www.tandfonline.com


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A. Jeang and F. Liang

leads to the absolute necessity of finding a systematic way to form a set of representative alternatives and an efficient rating process to help designers identify and select the most important design candidates from a set of representative alternatives. The issue becomes even more vital when multiple quality characteristics are considered for a set of design alternatives to satisfy the multiple scenario requirements during the NPD. Under these conditions, it is hard to create variable alternatives, rate these alternatives and make accurate design decisions in an economic, efficient and timely manner. Conventional approaches in dealing with the above difficulties include the usage of tools such as classification trees and combination tables for alternative exploration and selection (Ulrich and Eppinger 2008). The classification tree is used to split the design space of possible alternatives into a few separate classes to facilitate the selection process. A combination table offers a systematic way of grouping solution portions before conducting the selection process. The classification tree helps the design engineers divide the possible solutions into independent groups. The combination table guides the design engineers in selectively considering a mixture of solution portions. The aim is to explore the possibilities by organising and combining these solution portions. After alternative exploration, alternative evaluation and comparison is conducted with respect to performance requirements, customer needs and other quality characteristics. Based on the relative strengths and weaknesses of comparable alternatives, one or more alternatives are selected for further investigation and optimisation. In the practice of product development, weighted scores or multi-attribute utility scales are frequently used for ranking the corresponding alternatives (Keeney and Raiffa 1993, Ulrich and Eppinger 2008, Malek 2009). However, these tools for alternative exploration and alternative selection are very subjective and they have low efficiency. As is known, a considerable amount of research has appeared recently that focuses on the usage of DEA as a tool to rate comparable units (DMUs) in both public and private sector applications (Emrouznejad et al. 2008, Avkiran and Parker 2010). DEA is an approach for measuring the relative efficiency of DMUs that produces a single collective measure, which is a function of the inputs and outputs of operating processes. The major DEA models include the Charnes, Cooper and Rhodes (CCR) and the Banker, Charnes and Cooper (BCC) models (Charnes et al. 1978, Banker et al. 1984), the additive models (Charnes et al. 1985), the cone ratio models (Charnes et al. 1989, 1990), the DEA models with stochastic and chance constrained extensions (Land et al. 1994, Olesen and Petersen 1995, Cooper et al. 1996, 1998, 2002, Wu and Olson 2008, Wu and Lee 2010), the DEA models with inputs and the outputs are uncertain (Herrin et al. 2004), the DEA models for optimising dynamic quality characteristic systems (Tong et al. 2008), and the application in combination of fuzzy TOPSIS methodology and the DEA model for performance evaluation (Zeydan and Colpan 2009). This research will apply CCR and BCC models in developing a conceptual design approach with the aid of DOE and computer simulation. During the conceptual design stage, designers often find it difficult to know which of the potential alternatives to select for design optimisation because they lack the detailed design descriptions that most existing design tools require. A possible way to overcome this concern is to model the abstract characteristics of conceptual designs through computer-aided engineering (CAE) (Marion and Simpson 2009). In addition, recent developments in computer software and hardware, along with the increase in trained technicians in computerised applications, have strengthened the tendency to use computer experiments to analyse product performance during the early design stage. We will follow this trend by incorporating DOE and DEA. In this way, we aim to ensure that designers are able to create, rate and compare design alternatives, and then select a limited number of potential candidates efficiently and effectively. The expected outcome will be a short cycle of new product development. The proposed approach uses the design of experiments method (DOE) to explore possible design alternatives and analyse the alternatives via computer experimentation with ANSYS software. Then, the outputs from ANSYS software are incorporated with the DEA method for frontier analysis, which compares and selects the most efficient one in accordance with different market requirements. This introduction concludes the first section of the study. Section 2 briefly describes the rationale behind the research. Section 3 introduces the proposed approach. Section 4 provides an example to demonstrate its application and Section 5 presents a discussion of the results. Finally, Section 6 presents the conclusions.

2. Relevant rationales 2.1 Innovation funnelling process The innovation funnelling process is the core theme of Toyota’s product development approach (Sobek et al. 1999). A set of design alternatives is created at the very beginning of the funnelling process and evaluated as the

International Journal of Production Research

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process proceeds. Various quality characteristics are considered and different management levels are involved during the evaluation process. The purpose of evaluation is to select appropriate design candidates for the design optimisation stage that follows. This process is repeated several times in a loop before specific product designs are selected for design optimisation. Of course, cross-functional teams are needed to ensure that the handovers between functions occur rapidly and efficiently (Syan and Menon 1994, Sobek et al. 1999). However, Toyota’s innovation funnelling process does not include ways for generating alternatives; nor does it include evaluation techniques. The proposed approach in this study intends to overcome the aforementioned deficiencies.

2.2 The set-based design approach Because new products usually offer a firm the opportunity to gain extra profit, the NPD is an important tool for survival in a competitive global environment. As is known, the market tends to seek low cost, high quality and superior performance products with a short introduction interval to the market; thus, a workable and efficient product development approach is necessary. Normally, a number of design alternatives need to be created to meet the requirements of performance and the demands of customers. However, in traditional design practice, the designers often stop searching for possible alternatives in the early stage of NPD; thus, they are left with very limited and untypical design cases. We have to build an approach which can help the design team generate a set of design alternatives that represent the most typical cases in the early stage of the product development process (Sobek et al. 1999). This is the conceptual design stage, which precedes the detailed design stage of NPD. Conceptual design is well suited to a set-based approach. Although set-based approaches do not call for designers to make setback decisions, they do tolerate design freedom during the construction of relevant knowledge. In contrast to traditional design practice, a set-based design approach considers a broader range of design space from the beginning while delaying the commitment to a single alternative. The key to the success of this approach is to choose the proper space representation in drawing the boundaries around the possible design region. When this is done, the set-based approach compares alternative concepts created from the design region in order to identify and eliminate inferior alternatives so that more desirable designs can be chosen. In other words, the purpose of the space-narrowing method is to eliminate infeasible subspaces so that the best alternatives can be chosen from the initial design space (Nahm and Ishikawa 2006). Because alternative concepts are not presented with detailed descriptions, but rather are sets of alternatives defined by incomplete specifications, making exact comparisons between them is challenging (ibid). Sections 2.3 and 2.4 will consider this problem more fully. Before moving to these sections, let us discuss the mapping between the spaces of design variables and product performance. Since point to point mapping between design variable, vector X, and product performance, vector Y, is not a practical exercise, we have to confine vector X to a space with a specific design, R, so that product performance, Y, will also be in an expected space. This is the space to space design approach shown in Figure 1. Under a given design pattern, the exploration of space obviously increases the number of design alternatives. The performance space, Y, for the related design alternatives is limited by the design space allocated by design variable, vector X, and the optimal solutions found are located within this space.

Available design space formed by exploration

Y= f(X)

Design variable space

Available performance YR for design R , formed by available design variables

Product performance space

Figure 1. Space to space mapping between design variable X and product performance Y.

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A. Jeang and F. Liang YR: product performance space for design R

YS: product performance space for design S

YT: product performance space for design T


Available product performance boundary enlarged under multi-design alternatives

Figure 2. Space to space mapping between design variable X and product performance Y under multi-design alternatives.

Observably, the design decision based on space to space design must be superior to the design decision derived from point to point analysis. However, there is more than one available design space; for example, the possible S and T designs are not considered at the same time as design R is introduced. The performance space of designs R, S and T together may be larger than the performance space of a single design R. Accordingly, the optimal solutions found in this larger space may be better than the optimal solution found in the performance space of a single design R. Thus, a set of base designs containing a number of design alternatives needs to be introduced in order to enlarge the considered performance space so that the risk of obtaining the local optimal solution can be avoided. After expanding a single design to a set of designs, the available performance space is enlarged, as the example in Figure 2 shows. In contrast to local design optimisation for a single design R, this enlarged space makes possible the global design optimisation. Thus, in the proposed approach, determining a way to create a set of design alternatives to enlarge the available design space and the performance space is recognised as necessary.

2.3 Exploring design alternatives and performance spaces via a DOE matrix As the preceding discussion has made clear, an efficient and economical way to create a set of alternatives is needed for the successful application of the proposed approach. One possible way to create a sufficient amount of alternatives to represent the performance space of interest under multiple design variables is to use the DOE matrix. For example, two design variables, X1 and X2, are of interest for a specific design. To form the performance space, f(X1, X2), an experimental design matrix of 2k factorial design is adopted that explores four design alternatives, (1, 1), (þ1, 1), (1, 1) and (þ1, þ1), where þ1 and 1 symbolise a high level and a low level of each design variable. Because of the nature of the DOE matrix, the alternatives created through a combination of various levels of design variables must be exclusive (Montgomery 2009). In other words, the DOE matrix must construct a distinctive representative for each design alternative. Design variables set by following each combination must be typical alternatives. Then, the performance space can be described representatively through the set of typical design alternatives derived from the DOE matrix. In this way, the DOE matrix achieves an enlarged multi-space efficiently and economically.

2.4 Data envelopment analysis Our main concern in the next stage is to find a way to reduce the design alternatives in the evaluation process. The DEA method is usually used to evaluate the efficiency of a number of decision units characterised by multiple inputs and outputs. Instead of comparing decision units statistically in regard to average scores, the DEA is an extreme point method that compares one unit to the rest of the units, with weights chosen to favour the unit under


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consideration. Because of these characteristics, DEA is becoming an important decision tool for multi-quality characteristics analyses. DEA represents the efficiency of a given unit by measuring weighted amounts of outputs and inputs at the same time. The outputs are values for maximising criteria and the inputs are values for minimising criteria. Each unit should be allowed to select a set of weights that puts the unit itself in the most favourable position among the rest of the units. Thus, the efficiency of a considered unit, k, can be obtained as a solution to maximise the efficiency of unit k subject to the efficiency of all units being less than, or equivalent to, 1. The decision variables of this problem formulation are the weights that are the most favourable to unit k and can maximise the efficiency of unit k. However, the problem formulation only reaches a local optimal efficiency for alternative k with the associated weights. Thus, a do-loop process is conducted n times to compute all of the DMU’s efficiency. After the efficiencies for all of the alternatives are computed, all computed alternatives are ranked by decreasing values of efficiency. Two DEA formulations, CCR and BCC models, are considered in the presented research. Total efficiency is obtained for the CCR model (Charnes et al. 1978). For the BCC model, the technical scale and return to scale are measured (Banker et al. 1984). 2.4.1 Quality characteristics of the DEA application DEA is a non-parametric approach that produces a collective measure of relative efficiency among comparable DMUs, which comprise a set of inputs and outputs. The input is considered as a resource in the DEA environment. Normally, we assume that the output is increased as resources, the input, are added. However, in some cases, this assumption is not true. As is known, there are three kinds of quality characteristics: nominally-the-best (NTB), smaller-the-better (STB) and larger-the-better (LTB) (Phadke 1989). Unlike the quality characteristic LTB, the quality characteristics NTB and STB go against the aforementioned assumption in a normal situation where DEA is applied. Therefore, an appropriate DEA application will require an adequate transformation of the NTB and STB quality characteristics into the LTB quality characteristic (William et al. 2000, Cooper et al. 2006). The transformation equations for NTB and STB are: X¼

1 jZ T j

ð1Þ

where T is the target value for the quality characteristic value Z of NTB; X¼

1 Z

ð2Þ

where Z is the quality characteristic value of STB. 2.4.2 Decision-making space There are many factors that affect decision making during product design. One of these is the affordability of resources. Clearly, using affordable rather than prohibitive resources as inputs will yield more economical benefits. The characteristics of inputs can be divided into two categories (William et al. 2000, Cooper et al. 2006): (1) Outputs have a positive relationship with linear inputs. This relationship characterises the CCR model in the DEA method. The function representing this relationship can be expressed as: Y ¼ AX þ B

ð3Þ

where X is the input, Y is the output and A is a positive and constant value. (2) In contrast to the first category, A is not a constant value. Instead, it is a relationship that characterises the BCC model of the DEA method. This relationship is expressed as follows: A¼

dY dx

ð4Þ

where A value at X2 is less than A value at X1 when X2 is greater than X1. There are different management strategies at various stages of the product life cycle. In the early stage, the critical management tasks are to improve the performance of the product, since customers are usually willing to pay extra for higher performance products. This type of decision seeks to maximise the outputs at the equivalent inputs.

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Conversely, in the late stage of the product life cycle, with the product reaching the market saturation level, the prices are denominated by the producer because of competition. Critical management operations must now focus on cost reduction, which seeks to minimise inputs to satisfy the customers’ requirement. These two types of operation are called the MAX OUT and MIN IN optimisation modes in the DEA method. Obviously, there are two possible models, the CCR and BCC models, which describe each of these operations. In other words, there are four types of DEA method for a life-cycle design. These four DEA methods can also be considered as management and design views for product development. An example is shown on the left-hand side of Table 8 later in this paper. Unlike the quality characteristics STB and LTB, which are unidirectional, the NTB quality characteristic requires different product performance scenarios. These requirements can be achieved through performance-related parameters being established at multiple quality levels. Normally, the establishment of three levels should be sufficient to reflect the product performance requirement in a market. Thus, the proposed approach will include multi-NTB quality characteristic designs for DEA application. For convenience, two quality levels are considered in the following application. The example for the two levels assigned is shown at the top of Table 8.

3. The proposed approach The innovation funnel implementation process shown in Figure 3 guides us in developing the proposed approach. This approach includes creating the set-base design alternatives, which are explored with the DOE method, and limiting the created alternatives through design evaluation using the DEA technique. The proposed approach can be summarised as follows. First, design engineers have to create a set of design patterns i, i ¼ 1, 2, 3, . . . , m. Then, based on each design pattern, a set of associated design alternatives is created by referring to the DOE matrix. Assume n alternatives are created for each pattern. The number n is the same as the number of trials in the DOE matrix. As an example, consider that designers create patterns A, B, C, D, E, F, G, H, I, J, K and L. Alternatives A1, A2, A3, A4, A5, A6, A7, A8 for pattern A and L1, L2, L3, L4, L5, L6, L7, L8 for pattern L are created by referring to the

A1 A2 A3 A4 A5 A6 A7 A8

Design space and niche market

L1 L2 L3 L4 L5 L6 L7 L8

Design alternatives (DMUs) explored by DOE

Obtain simulated outputs from computer simulation Convert these outputs into DEA inputs and outputs

Figure 3. The proposed innovation funnel implementation process.

Define product scenarios and analyse DMUs by DEA

Alternative selection and design optimisation

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International Journal of Production Research Table 1. The symbolic representatives and descriptions for DEA quality characteristics. Symbolic representatives I1 I2 I3 O1 O2 O3 O4 O5

Descriptions of symbol representatives Cost of welding Cost of manufacturing Cost of material Weight of frame Stress induced, pedalling Energy absorption, pedalling Stress induced, riding Energy absorption, riding

Type of quality characteristics STB STB STB STB STB NTB STB NTB


Note: I represents DEA inputs and O represents DEA outputs.

DOE experimental matrix presented in Table 2. There are mn alternatives (DMUs) in total. A greater mn means that greater product performance space will result. Consequently, the possibility of gaining a set of favourable alternatives for further investigation is higher. Because design alternatives usually lack relevant descriptions during the conceptual design stage, a good way to start product development is to use computer-aided engineering (CAE) for the initial trials. The total mn alternatives are analysed by the CAE computer software ANSYS to achieve mn simulated runs. The simulated outputs of interest are the quality characteristics that reflect customer expectations and market requirements. The quality characteristics filed in Table 1 are an example. Although there are other powerful software programs capable of conducting similar simulations, for example Pro-E and COSMOS, the reason for adopting ANSYS software for the CAE application is that ANSYS provides built-in APDL (ANSYS Parametric Design Language). This built-in APDL enables designers to conveniently construct a product model in terms of various parameters (variables) during ANSYS usage (ANSYS 1997). The convenience of this built-in APDL makes the task of performing the ANSYS simulation for created alternatives, as listed in Table 5 for instance, and working through the DEA analysis for simulated outputs, as shown in Table 6, very efficient. These mn simulated outputs are further categorised into DEA inputs and outputs for frontier analysis. The analysis can be completed through Banxia Frontier Analyst Profession software. The theoretical basis of DEA along with the capability of the ANSYS and Banxia Frontier Analyst software, allow DEA analysis to work well with a great number of alternatives. Finally, a set of favourable alternatives are provided for final selection.

4. An application A bike-frame design is used as an example for demonstrating the proposed approach. A bike frame, the core structure of a bicycle, is basically composed of seven components: upper tube, down tube, stay tube, chain tube, head tube, seat tube and fork. Figure 4 depicts the graphic representation of the bike frame introduced in this study. The components of upper tube, down tube and seat tube are selected as the bases for exploring various design alternatives. The bike frame is a platform used as a conjunction spot when assembling the relevant systems, that is, the wheel system and cockpit system, which together with the bike frame form the whole bicycle structure. In addition, the bike frame itself acts as security insurance when riding a bicycle and it contributes to performance satisfaction. The appearance and weight of the bike frame are the features that usually draw a customer’s attention before buying a bicycle. The voice of the customer reveals the following concerns: (1) reasonable price, (2) minimum weight, (3) suitable pedalling comfort and (4) acceptable appearance and security. These concerns can be represented by a set of quality characteristics, which include (a) the price of the bike frame, (b) the weight of the bike frame, (c) the energy absorption of the bike frame in riding and pedalling conditions, and (d) the stress induced in riding and pedalling conditions. These multiple quality characteristics will therefore be treated as the response values of interest when designing a bike frame. The total cost, which belongs to the STB quality characteristic, is calculated by totalling the material and manufacturing costs of the bike frame, which should be as low as possible in order to be competitive.

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Figure 4. The typical bike-frame configuration.

The bike-frame weight is a crucial quality characteristic that also belongs to the STB quality characteristic. The service conditions of the bike-frame design focus on two concerns: riding and pedalling conditions. Because of these two concerns, the EN (European norm) horizontal and pedalling tests will be adopted to evaluate bike performance. There are two major outputs, stress and energy absorption, which can be derived after each EN test in the CAE environment. The quality characteristic of stress is STB and the quality characteristic of the bike frame’s energy absorption is NTB. These characteristics need to be designed in meeting the niche market’s requirements. The weight of the bike frame can be drawn from the CAE geometric data. In this study, ANSYS software is used for the CAE application (ANSYS 1997). The above quality characteristics can be further divided into two categories: DEA inputs and DEA outputs. The DEA input contains three items: cost of welding, I1, cost of manufacturing, I2, and cost of material, I3. The DEA output contains five items: weight of bike, O1, stress induced by pedalling condition, O2, energy absorbed by pedalling condition, O3, stress during riding, O4, and the energy captured by riding, O5. Table 1 tabulates the symbolic representatives, related descriptions and types of quality characteristic for the DEA input and DEA output.

4.1 The generation of DMUs In the presented research, the upper tube, down tube and seat tube components are open to designers in constructing various design alternatives. Aluminium materials are chosen for these tubes: aluminium alloy 6061-T6 with a Poisson ratio of 0.33, a density of 2:7e6 kgw/mm3 and Young’s modulus of 70,000 N/mm2. The available range of the outside diameter (OD) of the tube is 35–40 mm for the upper tube (factor I), 35–45 mm for the down tube (factor II) and 32–36 mm for the seat tube (factor III). The existing range of wall thickness (TH) for the upper tube, down tube and seat tube (factors IV, V and VI) is from 1.4 mm to 1.8 mm. Table 2 is a two-level factorial DOE matrix design used to analyse these six factors. The associated high and low levels for factors I, II, III, IV, V and VI are listed in Tables 3 and 4. At the beginning of the product development cycle, 12 design bike-frame patterns are proposed. Based on each design pattern, the engineering team creates eight design alternatives, which are explored by the DOE matrix given in Table 2. Thus, as indicated in Table 5, there are 96 design alternatives in total, which are considered as DMUs in the DEA analysis.

4.2 ANSYS simulation in analysing each DMU Tables 2, 3 and 4 are used together to form the inputs for the ANSYS simulation. The simulation is run by following sequentially the design alternatives introduced in Table 5. The ANSYS simulation is summarised as follows: (1) Apply loading and solve according to the suggestions from the EN pedalling and riding test method. In the pedalling condition, the vertical down direction 120 kgf loading and (torsion 5500 kgf-mm) X (axle positive direction) are applied on the BB right side end; apply the displacement constraint on the rear end with six degrees of freedom fixed entirely except for RZ, and on the fork end except for RZ and TX, as shown

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International Journal of Production Research Table 2. Two levels of DOE matrix for factors I, II, III, IV, V, and VI. Design matrix: 26–3 resolution three-factorial design Upper tube: OD

Down tube: OD

Seat tube: OD

Upper tube: TH

Down tube: TH

Seat tube: TH

I 1 1 1 1 1 1 1 1

II 1 1 1 1 1 1 1 1

III 1 1 1 1 1 1 1 1

IV ¼ (I)(II)(III) 1 1 1 1 1 1 1 1

V ¼ (II)(III) 1 1 1 1 1 1 1 1

VI ¼ (I)(III) 1 1 1 1 1 1 1 1


1 2 3 4 5 6 7 8

Table 3. The two levels of factors I, II and III.

Table 4. The two levels of factors IV, V and VI.

Factors: OD

Factors: TH

Low level: 1

High level: þ1

34 36 31

38 40 37

I II III Note: Unit ¼ mm.

IV V VI

Low level:1

High level:þ1

1.4 1.4 1.4

1.8 1.8 1.8

Note: Unit ¼ mm.

as Figure 5. In the riding condition, a 30 kgf horizontal force toward the rear is applied on each end of the fork, as shown in Figure 6. The displacement constraints are the same as the pedalling condition. (2) Review the results. The typical responses of the stresses and the energy absorption of the whole model in both the pedalling and riding conditions after the pedalling and riding test are shown in Figures 7 and 8. By following the level combination suggested in Table 2 with the level values given in Tables 3 and 4 as inputs for ANSYS software, design engineers can obtain the simulated outputs representing eight design alternatives with respect to each pattern. As mentioned in the preceding discussion, because there are 12 design patterns, a total of 96 outputs representing 96 DMUs is generated. The cost of welding, I1, and the cost of manufacturing, I2, are dependent, respectively, on the number of welding joints and the number of welding contours needing to be cut or machined. Then, with engineering experience, engineers can estimate the costs of I1 and I2 from the number of welding joints and welding contours. Because the cost of the materials, I3, is dependent on the weight of the bike frame, by knowing the weight of the bike frame, the estimated cost, I3, can be derived from the ANSYS software. The symbolic representatives O1, O2, O3, O4 and O5 are simulated outputs derived from the ANSYS software. After 96 design alternatives (DMUs) are analysed on the basis of engineering experience or by using ANSYS software, the interested outputs I1, I2, I3, O1, O2, O3, O4 and O5 are tabulated in Table 6. The three outputs I1, I2 and I3 are considered as DEA inputs and the other five outputs, O1, O2, O3, O4 and O5, are regarded as DEA outputs in the next DEA frontier analysis.

4.3 DEA frontier analysis After 96 outputs of DMU are produced, I1, I2, I3, O1, O2, O3, O4 and O5 are derived via ANSYS software; the next step is to evaluate these outputs and rank the efficiency of design alternatives (DMUs) by the DEA method. DEA frontier analysis can be completed with the aid of the Banxia Frontier Analyst software (Banxia Software 1994). The frontier analysis is executed for analysing the possible product performance spaces. As aforementioned above, we would like to demonstrate the flexibility and effectiveness of the proposed approach under uncertain market requirements with different product scenarios. The product scenarios are explored through a combination of two levels with controllable factors, R-stiff and P-stiff. The two related levels are listed in Table 7. Obviously, as Table 7

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Table 5. The proposed design patterns and the design alternatives of a bike-frame design. Design patterns A

Design alterns. A1 A2

Design patterns B


G

J

Figure 5. The pedalling condition.

Design patterns C

Design alterns. C1

B2

C2

A3

B3

C3

A4

B4

C4

A5

B5

C5

A6

B6

C6

A7

B7

C7

A8 D

Design alterns. B1

D1

E

B8 E1

F

C8 F1

D2

E2

F2

D3

E3

F3

D4

E4

F4

D5

E5

F5

D6

E6

F6

D7

E7

F7

D8 G1

E8 H1

H

I

F8 I1

G2

H2

I2

G3

H3

I3

G4

H4

I4

G5

H5

I5

G6

H6

I6

G7

H7

I7

G8 J1

H8 K1

K

L

I8 L1

J2

K2

L2

J3

K3

L3

J4

K4

L4

J5

K5

L5

J6

K6

L6

J7

K7

L7

J8

K8

L8


11


Figure 7. Typical contour of stress and energy absorption after pedalling test.

Figure 6. The riding condition.

Figure 8. Typical contour of stress and energy absorption after riding test.

shows, there are four combinations representing the possible product scenarios for achieving market requirements. In this example, we have four types of DEA application, as shown on the left-hand side of Table 8, and four product scenarios, as shown at the top of Table 8. Including the combinations in the middle of Table 8, there are, in total, 16 combinations for decision-making spaces, which represent different niche market requirements. Among the 96 outputs of DMU in Table 6, I1, I2 and I3 are used as DEA inputs and O1, O2, O3, O4 and O5 are used as DEA outputs to run front analyses repeatedly for 16 combinations, as given in Table 8.

5. Results and discussion In general, the marketing requirements are varied in different marketplaces. For example, a marketplace might call for a high-performance bike; this means that a high pedalling and riding stiffness and small energy absorption need to be included in the design consideration. These inclusions signify that R-stiff (O5) and P-Stiff (O3) must both be established at a high level in order to satisfy the market requirement for high performance of a product. In the case of Max optimisation with equivalent inputs for DMUs, as discussed above, the frontier analysis will adopt the MAX output with CCR mode to find the corresponding DMU’s efficiency. For example, the results in Table 8 show DMU A1 with 44 references, DMU C1 with 17 references, DMU C2 with 26 references and DMU F1 with 23 references. The reason we choose the top four DMUs from DEA analysis is for convenience. Because all four DMUs are 100% efficient, the rankings are solely based on the number of reference units. As is known, the higher the number of reference units for a DMU, the better the DMU will be among competitive candidates. Clearly, A1 should stand in the first rank among DMUs A1, C1, C2 and F1. Namely, A1 is the best choice for this case. It is also important, in

12


Table 6. Outputs from ANSYS software for 96 DMUs.


ANSYS outputs for 96 DMUs DMUs

I1

I2

I3

O1

O2

O3

O4

O5

A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 B6 B7 B8 C1 C2 C3 C4 C5 C6 C7 C8 D1 D2 D3 D4 D5 D6 D7 D8 E1 E2 E3 E4 E5 E6 E7 E8 F1 F2 F3 F4 F5 F6 F7 F8 G1 G2 G3 G4 G5 G6 G7 G8

45 50 50 55 50 55 55 60 45 50 50 55 50 55 55 60 45 50 50 55 50 55 55 60 50 55 55 60 55 60 60 65 45 50 50 55 50 55 55 60 45 50 50 55 50 55 55 60 50 55 55 60 55 60 60 65

21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 25 25 25 25 25 25 25 25 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 25 25 25 25 25 25 25 25

118.468 123.191 115.608 116.647 119.930 114.064 120.104 131.732 126.484 129.876 122.907 125.361 127.872 120.831 127.476 140.363 128.943 136.325 123.508 121.718 128.253 124.737 134.064 143.172 134.960 138.661 131.529 133.882 136.514 129.372 160.265 149.173 127.070 130.623 123.547 125.949 128.532 121.439 128.088 141.135 127.380 130.778 123.618 126.071 128.664 121.622 128.476 141.368 140.654 144.051 134.063 136.515 140.316 133.274 143.302 156.194

1.481 1.540 1.445 1.458 1.499 1.426 1.501 1.647 1.581 1.623 1.536 1.567 1.598 1.510 1.593 1.755 1.612 1.704 1.544 1.521 1.603 1.559 1.676 1.790 1.687 1.733 1.644 1.674 1.706 1.617 2.003 1.865 1.588 1.633 1.544 1.574 1.607 1.518 1.601 1.764 1.592 1.635 1.545 1.576 1.608 1.520 1.606 1.767 1.758 1.801 1.676 1.706 1.754 1.666 1.791 1.952

20.201 16.832 21.546 20.444 19.697 18.939 17.959 15.293 20.780 17.225 22.107 20.950 20.213 19.405 18.457 15.665 19.624 16.320 21.067 20.075 19.162 18.381 17.432 14.894 20.556 16.379 20.519 20.498 19.024 18.474 17.337 14.778 21.760 17.811 23.058 21.783 21.101 20.192 19.317 16.252 23.229 19.132 23.279 24.052 19.722 20.530 19.128 16.320 19.241 17.530 20.069 19.096 18.559 17.838 17.192 14.278

914.029 788.777 937.689 889.067 826.292 806.733 781.827 678.102 968.499 831.388 992.254 939.079 873.224 850.804 825.560 713.614 858.990 733.881 895.924 859.413 775.540 749.180 730.547 642.324 900.081 794.358 918.673 869.051 822.819 812.995 776.394 680.233 1058.039 900.740 1082.730 1023.108 951.594 925.200 900.784 775.106 1318.239 1110.914 1356.064 1281.410 1178.494 1140.635 1110.061 951.678 992.095 879.769 996.893 931.589 908.182 902.848 862.689 743.743

7.336 8.447 7.952 6.787 7.100 8.136 6.728 5.836 7.113 8.083 7.823 6.757 6.878 7.774 6.606 5.795 6.907 6.960 8.350 8.370 7.015 7.026 5.773 5.802 7.269 8.258 7.915 6.812 7.034 7.966 6.810 5.898 7.192 8.175 7.857 6.767 6.958 7.878 6.671 5.831 6.906 7.780 7.625 6.605 6.654 7.480 6.396 5.609 6.392 6.869 6.861 6.188 6.712 7.186 5.625 5.224

125.126 131.223 126.979 119.150 121.392 128.230 110.564 103.823 126.301 133.669 128.101 118.553 122.085 129.792 109.791 102.285 156.557 151.883 176.593 175.862 146.240 145.665 130.007 125.915 138.847 143.456 139.014 128.143 133.414 141.000 118.864 110.659 147.954 152.204 147.696 135.876 141.837 150.080 129.901 117.106 139.781 144.155 143.397 133.591 133.692 140.363 120.254 111.478 133.166 138.014 137.419 128.501 127.656 134.003 114.622 107.133 (continued )

13

International Journal of Production Research Table 6. Continued.


ANSYS outputs for 96 DMUs DMUs

I1

I2

I3

O1

O2

O3

O4

O5

H1 H2 H3 H4 H5 H6 H7 H8 I1 I2 I3 I4 I5 I6 I7 I8 J1 J2 J3 J4 J5 J6 J7 J8 K1 K2 K3 K4 K5 K6 K7 K8 L1 L2 L3 L4 L5 L6 L7 L8

45 50 50 55 50 55 55 60 45 50 50 55 50 55 55 60 48 53 53 58 53 58 58 63 53 58 58 63 58 63 63 68 50 55 55 60 55 60 60 65

21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 27 27 27 27 27 27 27 27 27 25 25 25 25 25 25 25 31 31 31 31 31 31 31 31

123.565 126.963 119.946 122.398 124.930 117.888 124.583 137.475 123.743 127.2257 120.177 122.601 125.162 118.091 124.761 137.738 128.870 132.686 125.258 127.572 130.351 123.170 130.026 143.337 132.196 137.964 130.054 131.723 135.039 127.214 133.214 148.476 129.671 133.844 126.287 128.483 131.377 124.079 130.824 144.492

1.545 1.587 1.499 1.530 1.562 1.474 1.557 1.718 1.547 1.590 1.502 1.533 1.565 1.476 1.560 1.722 1.611 1.659 1.566 1.595 1.629 1.540 1.625 1.792 1.652 1.725 1.626 1.647 1.688 1.590 1.665 1.856 1.621 1.673 1.579 1.606 1.642 1.551 1.635 1.806

21.569 17.892 22.962 21.699 20.961 20.146 19.095 16.159 21.126 17.585 22.516 21.297 20.567 19.786 18.733 15.881 21.580 18.441 22.040 22.894 19.948 19.511 18.868 15.447 19.492 16.713 21.401 20.612 19.278 18.702 17.171 15.114 21.887 17.224 21.362 21.988 19.224 18.819 18.547 15.026

1043.433 894.503 1069.952 1011.769 940.173 915.649 888.766 767.512 997.367 858.168 1023.634 969.432 900.296 878.044 852.138 737.500 1431.474 1207.050 1481.830 1400.823 1278.003 1236.228 1195.773 1028.161 679.716 598.418 703.282 687.516 625.513 616.762 598.356 537.077 1027.087 893.844 1075.002 1026.947 926.969 904.359 866.186 762.919

7.622 8.906 8.106 6.831 7.414 8.673 7.064 5.978 7.766 8.992 7.865 6.538 7.599 8.994 7.491 6.114 7.236 8.098 7.985 7.138 6.962 7.797 6.684 5.869 8.297 9.844 8.193 7.193 8.122 9.735 8.001 6.754 7.437 8.921 8.116 7.014 7.365 8.782 7.266 6.255

172.195 179.153 171.222 158.124 166.192 176.850 153.222 139.245 179.962 185.402 181.651 170.327 173.816 182.941 161.285 148.763 189.153 184.680 193.809 180.879 176.873 182.193 161.728 144.292 204.228 201.229 199.309 183.311 193.989 204.696 182.671 157.677 218.663 218.899 213.145 194.314 208.202 219.737 195.274 168.499

Table 7. The two levels of controllable factors P-stiff and R-stiff. Factors

Low level: 1

High level: þ1

P-stiff R-stiff

900 160

600 120

Note: Unit ¼ mm.

observing Table 8, to take note of the frequency of the DMU’s appearance. The more often it appears, the more robust the DMU will be for market uncertainty and the more likely it is that the goal of the life-cycle design will be realised. For example, DMU A1 with 15 appearances must be the first choice for decision makers in terms of lifecycle design and market uncertainty. It is thus apparent that the proposed approach enables decision-makers to

BCC

CCR

DEA method

C1 C2 J2 A1 J4 A8 K3 C7

Min input

Max output

J4 K3 A1 C7

Max output

C1 B8 A1 C2 A1 J4 A8 C7

38 31 30 27

J4 A1 C2 C1

J4 A1 C2 C1

29 27 27 26

50 49 42 23

50 49 41 26

40 36 34 24

40 31 27 25

51 44 27 23

51 44 27 23

K3 A8 C7 A1

C1 C2 A1 F1

K3 A1 C2 C7

K3 A1 C2 C7

31 29 27 27

34 25 25 21

48 40 26 23

48 40 26 23

A1 A8 A4 C7

A1 C1 B8 C2

A1 C2 F1 C1

A1 C2 F1 C1

38 28 25 21

34 30 29 24

44 26 23 17

45 26 23 23

No. of reference unit Design altern. with 100% efficiency No. of reference unit

Design altern. with 100% efficiency

No. of reference unit

Design altern. with 100% efficiency

No. of reference unit

Design altern. with 100% efficiency J4 K3 A1 C2

1

P-rigidity level 1

P-rigidity level

1

1

R-rigidity level

P-rigidity level

1

1

R-rigidity level

P-rigidity level

1

R-rigidity level

1

4

R-rigidity level

3

2

1

Min input

Product scenario

Table 8. DEA analysis for 16 combinations.


14 A. Jeang and F. Liang


15

choose the most appropriate alternative flexibly and efficiently in satisfying various market requirements. However, even in a situation where the market requirement is not clear, the access to a wide range of sufficient alternatives and four DEA methods for effective life-cycle analysis provides decision-makers with the most opportunities to satisfy buyers through a bargaining process. In a scenario where a soft bike is required for women riders in city transportation, R-stiff and P-stiff are set at a low level (1, 1) in order to gain a competitive market price; in other words, the MIN should be adopted in the optimisation mode. As the example given in Table 8 shows, the combination of the expected product performance (1, 1) and the Min of the model in CCR mode is considered for DEA analysis. Table 8 has J4 with 50 references, K3 with 49 references, A1 with 41 references and C2 with 26 references. There is no difference among the alternatives. Unless the market requirements are clearly defined, the alternatives will be considered the same. In order to reflect the advantages and disadvantages of the proposed approach, the conventional weighted score approach is adopted for comparison analysis. Weighting alternatives for selection is not a new idea and it is popular in practical application. The total score for each alternative is the sum of the weighted scores (Ulrich and Eppinger 2008):


Sj ¼

K X

wk Rkj

ð5Þ

k¼1

where j is 1, 2, 3, . . . , J. Rkj is the rating of alternative j for the kth criterion, and wk is the weighting for the kth criterion. Capital K is the total number of criteria for a design. Sj is the total score for alternative j, and capital J is the total number of alternatives. The rank is based on the order of the total score from Equation (5). For comparison analysis, assume the rating scale ranges from 1 to 5. The weights of the criteria are listed in Table 9. Because the conventional approach lacks alternative exploration, only 12 alternatives are considered for comparison. In this case, K is the twelfth alternative. The explored alternatives (DMUs) are A1, B1, C1, D1, E1, F1, G2, H1, I1, J1, K2 and L1. Table 9 shows the scoring matrix with the conventional weighted score approach. The best alternative selected for detailed design is alternative I1. Because the conventional weighted score approach does not consider the impact of life-cycle design and market-requirement uncertainty, the decision-maker will always select I1 regardless of the 16 combinations indicated in Table 8. For the purpose of comparison, Table 10 summarises the best selections and corresponding rankings of the DMUs in the proposed approach and the conventional weighted score approach. The selected alternatives (DMUs) in the proposed approach are always ranked first because this approach is able to explore a set of appropriate alternatives and thus absorb the impact of life-cycle design and market-requirement uncertainty. Conversely, the majority of selected alternatives (DMUs) based on the conventional weighted score approach are ranked in the range of 38 to 55. In addition, the weights assigned by the conventional weighted score approach are very subjective as compared to the weights determined by the proposed approach. Given the impact of life-cycle design and market requirement uncertainty, this deficiency makes the design decision even worse. Production costs incurred beyond the design phase can only have little influence on the final product cost. Likewise, product quality cannot be added to a product unless it is pre-designed into the product before production. In addition, customers normally prefer the newest products on the market. Thus, for market competitiveness, an important concern other than quality and cost is to reduce the product development cycle time required to bring a new product to market. The proposed approach is introduced to resolve these concerns. The results of the presented example demonstrate that this approach enables design alternatives to be created for selecting and narrowing the investigation, and thus satisfying market requirements efficiently and economically. For the ease of comprehension and application, the proposed approach is summarised in six steps that illustrate the example described above. Basically, the steps are linearly coincident with the proposed innovation funnel implementation process shown in Figure 3. The implementation process runs through the stages of the proposed approach. These stages include creating the set-base design alternatives explored with the DOE method and reducing the created alternatives to a limited number of alternatives through design evaluation using the DEA technique. The material below itemises and explains the steps involved: Step 1: design.

Define the product of interest for design. For example, Figure 4 is a graphic representation of a product

Step 2: Collect customer voices and convert them into technical terms. For example, regarding a bike design, customers are mostly concerned with the price, weight, comfort, appearance and security of the bike.

DMU

A1

B1

C1

D1

E1

F1

G1

H1

I1

J1

K1, reference

L1

0.10 0.10 0.10 0.20 0.10 0.10 0.15 0.15

5 5 5 5 2 2 4 1

3.65 3 No

0.5 0.5 0.5 1 0.2 0.2 0.6 0.15

5 5 4 4 2 2 4 1

3.35 5 No

0.5 0.5 0.4 0.8 0.2 0.2 0.6 0.15

5 5 4 3 3 2 5 2 3.55 4 No

0.5 0.5 0.4 0.6 0.3 0.2 0.75 0.3

4 4 3 3 2 2 4 1 2.85 10 No

0.4 0.4 0.3 0.6 0.2 0.2 0.6 0.15

5 4 4 4 2 1 4 1 3.15 7 No

0.5 0.4 0.4 0.8 0.2 0.1 0.6 0.15

5 4 4 4 1 1 5 1 3.20 6 No

0.5 0.4 0.4 0.8 0.1 0.1 0.75 0.15

4 4 2 1 4 2 5 1 2.70 11 No

0.4 0.4 0.2 0.2 0.4 0.2 0.75 0.15

5 5 5 5 2 1 4 2 3.70 2 No

0.5 0.5 0.5 1 0.2 0.1 0.6 0.3

5 5 5 5 2 2 4 2 3.80 1 Yes

0.5 0.5 0.5 1 0.2 0.2 0.6 0.3

BCC

CCR

DEA method

Min input Max output Min input Max output

Product scenario

J4 J4 C1 J4

Best DMU I1 I1 I1 I1

1 1 1 1

38 38 27 32

DMU rank

Conventional approach

Proposed approach DMU rank

1

P-rigidity level

Best DMU

1

R-rigidity level

1

J4 J4 C1 A1

Best DMU

1 1 1 1

DMU rank

Proposed approach

P-rigidity level

R-rigidity level

2

I1 I1 I1 I1

Best DMU

41 41 41 48

DMU rank


1

1

K3 K3 C1 K3

Best DMU

1 1 1 1

DMU rank

Proposed approach

P-rigidity level

R-rigidity level

3

I1 I1 I1 I1

Best DMU

42 42 10 29

DMU rank


1

1

Table 10. Summary of the best selections and the corresponding ranks of the proposed approach and the conventional weighted score approach.

Total score Rank Selection for next detailed design

I1 I2 I3 O1 O2 O3 O4 O5 4 3 4 4 2 1 4 2

3 3 3 3 3 3 3 3

A1 A1 A1 A1

Best DMU

1 1 1 1

DMU rank

Proposed approach

P-rigidity level

4

0.3 0.3 0.3 0.6 0.3 0.3 0.45 0.45 3.00 9 No

R-rigidity level

3.10 8 No

0.4 0.3 0.4 0.8 0.2 0.1 0.6 0.3

1

1

2.70 11 No

0.4 0.1 0.4 0.6 0.2 0.1 0.6 0.3

I1 I1 I1 I1

Best DMU

46 46 31 55

DMU rank


4 1 4 3 2 1 4 2

Symbolic Associated Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted Rating Weighted representative weight score score score score score score score score score score score score score score score score score score score score score score Score score

Input and output

Table 9. Scoring matrix with the conventional weighted score approach.



17

Technically, these concerns can be converted into a set of quality characteristics, which include the cost of the bike frame, the weight of the bike frame, the energy absorption of the bike frame in riding and pedalling, and the stress induced in riding and pedalling. After deriving this set of quality characteristics, classify these characteristics into the DEA inputs and DEA outputs, as the tabulation in Table 1 shows. Step 3: Create a set of design patterns i, i ¼ 1, 2, 3, . . . , m. Expand each pattern to its associated n DMUs through the matrix in Table 2. The matrix suggests how the levels can be combined to form n DMUs. For example, m is 12 patterns. As the dual levels in Tables 3 and 4 indicate, the number of explored alternatives for each pattern, n, is eight. Thus, a total of 12 8 DMUs are used for DEA analysis.


Step 4: With the combined levels introduced in Step 3 as ANSYS inputs for computer simulation, a set of ANSYS outputs can be obtained. These are the quality characteristics of interest for the following DEA analysis in Step 5. The quality characteristics in the set of ANSYS outputs are further divided into DEA inputs and DEA outputs for the purpose of front analysis through Banxia software. For example, the associated quality characteristics I1, I2, I3, O1, O2, O3, O4 and O5 for a set of 96 ANSYS outputs associated with 96 DMUs (see Table 6) are categorised into DEA inputs for the first three characteristics and DEA outputs for the remaining characteristics. Step 5: Every possible product scenario corresponding to market requirements should be given in advance in order to run frontier analysis in DEA. In order to cover all the possibilities, the market requirements are explored through a combination of market necessitated levels using the DOE matrix, similar to the expansion made via the DOE matrix in Step 3. The example given in Table 7 of four combinations derived from two levels presents the most typical representatives that satisfy the market requirement for all possibilities. They appear at the top of Table 8. Step 6: Because there are different management strategies associated with various periods in a life-cycle design, the four types of DEA method mentioned above are considered. They are CCR-min input, CCR-max output, BCC-min input and BCC-max output. As the example shown in Table 8 indicates, with four DEA methods and four typical market representatives, there are 16 combinations in total. Thus, 96 DMUs will go over each combination for DEA analyses. The results are also shown in Table 8. Because there are mn alternatives (DMUs) in total, a greater mn represents a greater resulting product performance space. Consequently, the possibility of gaining a set of favourable alternatives for further investigation is higher. The total mn alternatives are analysed by ANSYS to achieve mn simulated outputs. These mn simulated outputs are further categorised into DEA inputs and outputs for frontier analysis. The analysis can be completed using Banxia Frontier Analyst Profession software. Finally, a set of favourable alternatives is provided for the selection.

6. Conclusion The proposed approach with the innovation funnel process incorporates several techniques, including engineering domain knowledge, design of experiment (DOE), computer simulations and data envelopment analysis (DEA), to form an efficient product development scheme during the conceptual design stage. Basically, the proposed approach intends to create a set of design alternatives through DOE exploration based on design patterns created at the beginning of the funnelling process. Then, with the DEA method, the set of design alternatives is evaluated and reduced to a specific set of alternatives in the design evaluation stage. The final design optimisation is given in the detailed design stage. A bike design was selected as an example for demonstrating the proposed approach. The results show that a set of representative DMUs can be efficiently explored by DOE and effectively analysed using ANSYS computer software. Then, DEA performed frontier analysis to measure relative efficiency under various market requirements. For the readers’ application, the advantages and disadvantages are listed as follows. The disadvantages may be worthwhile considering for future extension in related research works.

6.1 The advantages Because alternatives are created systematically through DOE exploration and DEA selection, the proposed approach is more efficient and less subjective than other approaches. The DEA model is normally used to evaluate existing problems and activities. CAE generally proves its strength for incomplete descriptions during

18



conceptual design. The joint use of DEA and CAE in the proposed approach makes possible the ongoing NPD through DEA. Because of the shareable properties of ANSYS, Banxia and DOE, with partial interference from design engineers, the design automation is achievable. The arrangement in Table 8 shows the flexibility of the product design in adapting to market uncertainty. By using the features of ANSYS and Banxia, the proposed approach can deal with a huge number of design problems automatically.

6.2 The disadvantages The proposed approach assumes that interaction among the inputs and outputs does not exist in DEA analysis. In reality, this interaction does exist during the exercise of product design. If we can redefine the quality characteristics of interest to remove the element of interaction, the number of inputs and outputs can be reduced to a small number, which will enable resolution improvement to be realised in DEA analysis. Another disadvantage pertains to the assumption that the inputs and the outputs are certain values. In fact, the inputs and the outputs are uncertain in some cases, especially during the earlier stage of product development (Del Prete et al. 2010). Assume the design variables form in a certain probabilistic distribution. Let the parameter values of design variables be generated randomly for analysis through engineering experience or ANSYS software, as was done in section 4.2. The values I1, I2, I3, O1, O2, O3, O4 and O5 from the analysis will form a probabilistic distribution as well. Then, we can conduct a robust DEA analysis for uncertainty that makes the proposed approach more applicable during practical exercise. The basic DEA model assumes that the inputs and outputs are static under operational environments; however, the inputs and outputs for product development are dynamic in multi-operation environments, particularly for a life-cycle product application. Also, the proposed approach assumes that the design patterns should be formed by the designers before alternative exploration through a DOE matrix. However, success in pattern formation is mainly dependent on the engineer’s domain knowledge and design experience. This makes the product development uncertain. The term quality has generally been defined as the level of satisfying customer requirements. The inputs and outputs in our proposed approach only include quantitative measures. However, since qualitative measures provide a richer picture of product performance than do quantitative measures, qualitative measures are becoming ever more important indicators of how a product’s performance is perceived by customers. Hence, the inputs and outputs in qualitative measures have to be introduced in our proposed approach for effective DEA analysis. Finally, to ensure the DEA is applicable in the proposed approach, the number of DMUs should be at least twice the amount of the sum of inputs and outputs.

Acknowledgements This research was carried out in the Design, Quality and Productivity Laboratory (DQPL) at the Department of Industrial Engineering and Systems Management of Feng Chia University, Taichung, Taiwan, ROC.

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