Using the theory of inventive problem solving to ...

Computers & Industrial Engineering 85 (2015) 227–234

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Using the theory of inventive problem solving to brainstorm innovative ideas for assessing varieties of phone-cameras q Chih-Hsuan Wang ⇑ Department of Industrial Engineering & Management, National Chiao Tung University, Taiwan

a r t i c l e

i n f o

Article history: Received 25 October 2014 Received in revised form 21 February 2015 Accepted 5 April 2015 Available online 9 April 2015 Keywords: Smartphone Digital camera TRIZ Association rule Conjoint analysis

a b s t r a c t Today, to survive in an extremely turbulent business environment, traditional products characterized by limited capabilities cannot satisfy diverse customer requirements. In particular, it is observed that the boundaries between smart phones, digital cameras, and tablets are becoming more and more blurred than before. Therefore, for attracting the ad-hoc segments, global companies began to develop hybrid pad-phones and phone-cameras. Unfortunately, some of these products are facing poor sales without incurring much market attention. In order to overcome the aforementioned difficulty, this paper presents a novel framework to reduce the gaps between producer expectation and user perception. By means of the TRIZ (the theory of inventive problem solving), a contradiction matrix is applied to handle engineering conflicts among multi-functional alternatives to seek inventive solutions. Then, ARM (association rule mining) is conducted to identify critical features that formulate customer dissatisfaction (purchase intention). Finally, CA (conjoint analysis) is employed to derive customer utilities for benchmarking varieties of design concepts. In summary, the proposed framework cannot only help product planners efficiently generate innovative ideas, but also effectively justify the validity of design concepts. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Owing to dynamically changing customer desires coupled with rapid technology advances, global markets are full of various product offerings (i.e. smart phones, tablets, digital cameras, and wearable devices) to fit the ad-hoc segments. In the past, companies provided products with high quality, low cost, and at most, courteous after-sale service to satisfy market majorities. Today, companies need to customize products or services to satisfy distinct segments to consolidate their market shares (Crawford & Benedetto, 2008; Wang & Chen, 2012; Yang & Shieh, 2010). For instance, Nokia and Motorola have lost huge markets in smart phones and were merged with Micro-soft and Google, respectively. In contrast, Apple and Samsung have gained their brilliant revenues arising from various hot-selling consumer electronics. As reported by the Economic Times in 2014, compact camera sales fell by 30% and camera OEM/ODM makers were forced to trim production. Apparently, the popularity of smart phones resulted in poor product sales of low-end digital cameras since the boundary between smart phones and other consumer electronics has become q

This manuscript was processed by Area Editor Turkay Dereli.

⇑ Address: 1001 University Road, Hsinchu 30013, Taiwan. Tel.: +886 03 5712121 57310; fax: +886 03 5722392. E-mail addresses: [email protected], [email protected] http://dx.doi.org/10.1016/j.cie.2015.04.003 0360-8352/Ó 2015 Elsevier Ltd. All rights reserved.

more and more blurred than before (Wang & Wang, 2014; Wang & Wu, 2014). As we know, to survive in an intensively competitive environment, the key capability involves incorporating customer perceptions or preferences into the process of product positioning and differentiation (Alptekin, 2012; Lai, Lin, Yeh, & Wei, 2006). However, to successfully position products in a crowded and fragmented market, firms need to transform diverse customer requirements into attractive products (portfolios of features). Specifically, ‘‘differentiation’’ is creation of tangible or intangible characteristics (i.e. aesthetic features, functional performances, after-sale services, and selling prices) between a firm and its competitors whereas ‘‘positioning’’ refers to implementing a set of tactics to ensure these characteristics can occupy a unique position in the minds of customers (Kwong, Luo, & Tang, 2011; Wang, 2015). That means, positioning does not refer to what a company does to a product; rather, it is what a company does to the prospect of customers (Lilien & Rangaswamy, 2003). For instance, for acquiring the niche segments, brand companies have developed several cross-boundary alternatives, such as Sony’s QX series (i.e. QX10, QX100, and QX 30) and Samsung’s phone-cameras (i.e. S4 zoom, galaxy camera, K-zoom). Unfortunately, most of the above-mentioned alternatives are facing extremely bad sales without conforming to firms’ original expectations. In practice,

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234

identifying key features which formulate customer dissatisfaction with existing multi-functional alternatives is of importance to understand the underlying reasons behind poor sales (Wang, 2015). Ideally, designing an alternative that outperforms competitors’ products in all dimensions is perfect. In reality, due to physical constraints or limited resources, an improved feature is often accompanied with worsening feature(s). After reviewing the past studies, numerous skills have been suggested to reduce the gap between users’ requirements and producers’ alternatives, such as quality function deployment, multicriteria decision making, conjoint analysis, Kano model, Kansei engineering, customer co-creation, and multi-agent automated negotiation (Isßıklar & Büyüközkan, 2007; Ayag˘ & Özdemir, 2009; Altun, Dereli, & Baykasog˘lu, 2013; Ries, 2011; Wang & Chen, 2012; Wang & Wu, 2014). In this context, a TRIZ (theory of inventive problem solving) based framework is proposed to generate innovative ideas for designing phone-cameras and to benchmark these new ideas (design concepts) for better assessment. More importantly, several critical issues are addressed as follows:  How to handle the trade-offs between an improved feature (i.e. optical zoom) in smart phones and other worsening characteristics (i.e. thickness or weight)?  Which features are the most critical to formulate customer dissatisfaction with existing alternatives (i.e. Sony’s QX series or Samsung’s phone-cameras)?  How to redesign potential alternatives and justify marketing validity for acquiring diverse user requirements of the niche segments? The rest of this paper is structured as follows. Section 2 briefly reviews the concept of TRIZ. Section 3 introduces the proposed framework and relevant techniques. An industrial case study on designing and assessing varieties of phone-cameras is illustrated in Section 4. Concluding remarks are drawn in Section 5. 2. Overview of the theory of inventive problem solving (TRIZ) TRIZ was developed by the Soviet inventor Altshuller (1984), who had analyzed over 400,000 patents to construct a systematic framework (see Fig. 1) composed of a contradiction matrix (Table 1), 39 engineering parameters (Table 2), and 40 innovative principles (Table 3). Specifically, TRIZ includes a practical methodology, tool sets, a knowledge base, and model-based technology for generating new ideas and solutions for problem solving (Wikipedia, http://en.wikipedia.org/wiki/TRIZ; http://www. qaiglobalservices.com/downloads/Innovation_Management.pdf?). As indicated by Fig. 2, the entire process to implement the TRIZ includes: (1) abstraction-converting specific problems into general problems, (2) mapping-finding typical solutions for solving general

F

Substance 2

S1

S2

Fig. 1. TRIZ’s substance-field model.

39 system characteristics

Worsening features 1

Improving features 1 2

2

38

39

N/A N/A N/A

38 39

N/A N/A

%N/A means ‘‘not applicable’’.

problems, and (3) concretizing-projecting typical solutions into specific solutions that can be tailored to specific domain problems. In brief, TRIZ presents a systematic approach for analyzing challenging problems where inventiveness is needed and provides a range of tools for finding inventive solutions. To the best of our knowledge, TRIZ has been widely applied to different industries, including product/process development (Sheu & Hou, 2013; Yeh, Huang, & Yu, 2011; Zhang, Yang, & Liu, 2014), service innovation (Su & Lin, 2008), and eco-innovation (Chen & Chen, 2007; Chai, Zhang, & Tan, 2005). Furthermore, some of the above-mentioned studies have integrated QFD (quality function deployment) with TRIZ to analyze the interrelationships between customer requirements and engineering characteristics and the conflicts among them (Yamashina, Ito, & Kawada, 2002). In this context, TRIZ is adopted to accomplish phone-camera design by employing a contradiction matrix. The first step is to confirm the improved feature(s) and the worsening feature(s). Despite the market share of the low-end digital cameras have been mostly replaced by smart phones, consumers are still unsatisfied with smartphone’s photo quality (Wang & Wu, 2014). For example, the area of image sensor like CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) is only 1/3.2 inch (resulting in weak light-sensitivity). Moreover, most smart phones lack of optical-zoom capability (digital zoom usually leads to poor photo quality). For improving smartphone’s photo capability, Samsung recently developed a series of Android system based phone-cameras (i.e. S4 zoom, K-zoom, and galaxy camera). Meanwhile, Sony also designed a portable QX series (separable camera-lenses). In this paper, TRIZ is adopted to address these examples and explain the underlying reason behind poor sale of Samsung’s phone-cameras. In Section 4, three improved features (i.e. #24 – loss of information, #28 – accuracy of measurement, and #37 – difficulty of detecting & measuring) and two worsening features (i.e. #2 – weight of nonmoving object and #4 – length of nonmoving object) are addressed. 3. Proposed methodologies In order to design multi-functional products (phone-cameras), this study proposes a TRIZ (the theory of solving inventive problems) oriented framework to incorporate customer perceptions and preferences into the entire process. For convenience, the framework shown in Fig. 3 is operated as follows:

Field

Substance 1

Table 1 TRIZ’s contradiction matrix.

 Initially, the concept of TRIZ is applied to seek innovative solutions for tackling engineering conflicts between improved features and worsening features.  Secondly, association rule mining (ARM) is conducted to recognize key features that characterize customer dissatisfaction with existing alternatives.  Thereafter, conjoint analysis (CA) is employed to elicit customer utilities of key features for assessing and benchmarking competitive alternatives (varieties).

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234 Table 2 TRIZ’s 39 system features (engineering parameters). Geometry physics

Negative parameters

Positive parameters

1. Weight of moving object 2. Weight of nonmoving object 3. Length of moving object 4. Length of nonmoving object 5. Area of moving object 6. Area of nonmoving object 7. Volume of moving object 8. Volume of nonmoving object 9. Speed 10. Force (intensity) 11. Tension (pressure) 12. Shape 17. Temperature 18. Brightness 21. Power

15. 16. 19. 20. 22. 23. 24. 25. 26. 30. 31.

13. 14. 27. 28. 29. 32. 33. 34. 35. 36. 37. 38. 39.

Durability of moving object Durability of nonmoving object Energy spent by moving object Energy spent by nonmoving object Waste of energy Waste of substance Loss of information Waste of time Amount of substance Harmful factors acting on an object Harmful side effects

Stability Strength Reliability Accuracy of measurement Accuracy of manufacturing Manufacturability Convenience of use Repairability Adaptability Complexity of device Difficulty of detecting & measuring Level of automation Productivity

Table 3 TRIZ’s 40 innovative principles. Spatial separation

Temporal separation

Conditional separation

Systematic separation

1. Segmentation 2. Extraction

9. Prior counter-action 10. Prior action

5. Combining 6. Universality

3. Local quality 4. Asymmetry 7. Nesting

11. Cushion in advance 15. Dynamicity 16. Partial or overdone action

13. Inversion 14. Spheroidality

18. Mechanical vibration 19. Periodic action

12. Equipotentiality 28. Replacement with a mechanical system 31. Use of porous material 32. Changing the color 35. Transformation of the physical and chemical states of an object 36. Phase transformation 38. Use strong oxidizers

17. Moving to a new dimension 24. Mediator 30. Flexible membranes or thin film

20. 21. 26. 29. 34. 37.

General problems (39 system features)

Continuity of a useful action Rushing through Copying Pneumatic or hydraulic construction Rejecting and regenerating parts Thermal expansion

General solutions (40 inventive principles)

Specific problems

Specific solutions

(technical contradiction)

(concrete methods)

Fig. 2. TRIZ’s general process.

 Finally, managerial insights are generated to assist product practitioners in designing and developing attractive phonecameras.

39. Inert environment 40.Composite materials

8. Counterweight 22. Convert harm into benefit 23. Feedback 25. Self-service 27. Inexpensive, short-lived object for expensive, durable one 33. Homogeneity

Applying the concept of TRIZ to generate inventive ideas for designing varieties of phone-cameras

Conducting association rule mining to identify significant attributes of customer satisfaction with existing alternatives

Employing conjoint analysis to derive customer utilities of product attributes for recognizing potential alternatives

Eliciting managerial insights on developing next-generation phone-cameras for acquiring the niche segments

3.1. Conducting association rule mining to identify critical attributes Fig. 3. The proposed research framework.

Association rule mining, originated from affinity analysis, is also called market basket analysis. The goal of this method is to examine the issue of ‘‘what goes with what’’. A typical example comes from using bar-code scanners in supermarkets to automatically look for associations between purchased items, particularly in an implicit form of A ) B. In brief, the idea behind an association rule is to examine all possible rules among items in terms of an ‘‘ifthen’’ format in the following question: If item A (antecedent) has been purchased, what is the possibility of item B (consequent) being

later? Here, the antecedent and consequent are disjoint sets of product items characterizing the entire transaction database. In order to generate frequent itemsets, the most famous and classic algorithm is the Apriori algorithm (Agrawal, Imielinski, & Swarmi, 1993). The key idea of Apriori algorithm is to use (k  1) frequent item sets to generate k candidate item sets (the step of joining) and then

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remove infrequent sets (like pruning) after checking their critical measures. Notice that the subsets of large itemset(s) are also frequent. In practice, three common metrics (Bae & Kim, 2011) for measuring the association between the antecedent (A) and the consequent (B) are defined below:

SupportðAÞ ¼

Confidence ¼

#fAg ; n

SupportðBÞ ¼

#fBg ; n

ð1Þ

might be possibly generated. To derive the part-worth utilities of six attributes, it is infeasible to ask an evaluator to prioritize 144 alternatives at a time. Hopefully, by means of fractional factorial design, the required number for ranking alternatives can be significantly reduced to only 16. A general form of conjoint analysis (CA) for an alternative can be modeled as follows (Wang & Wu, 2014):

U k ¼ b0 þ

SupportfA [ Bg ; SupportfAg

ð2Þ

SupportfA [ Bg Cov erge ¼ ; SupportfBg

ð3Þ

where n is the number of transaction records in the database, the support represents the probability of object A (object B) appearing in the records, and the confidence (coverage) is equivalent to the conditional probability that measures the joint probability given object A (object B) has been captured in advance. In this study, object A represents a specific product feature while object B denotes the dissatisfaction response of an invited respondent. In particular, two measures including ‘‘confidence’’ and ‘‘coverage’’ are derived to recognize critical (significant) attributes that formulate customer dissatisfaction with a testing phone-camera. 3.2. Employing conjoint analysis to derive customer utilities of critical attributes Conjoint analysis (Luce & Turkey, 1964) is one of the most popular techniques for measuring diverse customers’ preferences among multi-attribute products or services (Fogliatto, Giovani, & Siverira, 2008). When a product is decomposed into independent multi-attributes, its overall utility can be obtained by aggregating the part-worth utilities of attributes associated with their corresponding levels. Let us illustrate a simple case to explain its applicability. Suppose that a product is characterized by six attributes (A1–A6) associated with specific levels (see Fig. 4): intuitively, a maximal number of 144 (2  2  3  3  2  2) varieties

L1 L2

ð4Þ

i¼1 j¼1

where U k stands for alternative k’s overall utility, b0 is a regularized constant, uijk is a multi-attributed part-worth of alternative k associated with attribute i and level j, m represents the number of attributes, and n denotes the number of corresponding levels. In order to attain the importance degree of various attributes, it is commonly believed that attributes with a larger range of part-worth values should have a greater impact on the overall utility. Thus, the importance weight (Wi) of attribute i can be obtained by normalizing its range (Ri) of part-worth utilities (Wang, 2015):

Ri W i ¼ Pm

i¼1 Ri

;

where Ri ¼ maxðuij Þ  minðuij Þ j

As it was mentioned above, the market share of low-end digital cameras has been replaced by powerful smart phones. Meanwhile, Samsung is very ambiguous to promote Android powered cross-

A3

A4

A5

A6

Utility decomposition into separate

*

*

*

*

*

*

part-worth values of core attributes

*

*

*

*

*

*

with associated levels

*

*

A3

A4

A5

A6 l16

A2 l12

l21

l22

A1

A2

l26

D3

Generating potential concepts through various combinations of attributes with associated levels

D144

D1 D2

A3

A4

A5

ð5Þ

4. An industrial example for designing and assessing phonecameras

A2

A1 l11

j

Intuitively, the larger the range (maximal utility minus minimal utility), the more important an attribute is since the attribute thus impacts consumers in a more significant way. In this paper, ARM is used in advance to select ‘‘critical’’ features. Then, only these features coupled with associated levels are reserved and passed to next phase (experimental design of conjoint analysis). Furthermore, CA is applied to derive customer utilities and justify the marketing validity of an innovative design concept (benchmarking with existing alternatives).

A1

L3

D1 D2

m X n X uijk ;

A6

Simplifying concept evaluation (trade-off decisions) through the

D16 Fig. 4. The process of implementing conjoint analysis.

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234 Table 4 TRIZ’s inventive solution for designing phone-cameras. Worsening feature Improving feature

24. Loss of Information 28. Accuracy of measurement 37. Difficulty of detecting & measuring

2. Weight of a nonmoving object ? 5, 10, 35 2. Weight of a nonmoving object ? 25, 66, 28, 35 2. Weight of a nonmoving object ? 1, 6, 13, 28

boundary phone-cameras. Unfortunately, unlike Samsung’ popular smartphone series (i.e. S2, S3, S4, and S5), the phone-cameras (i.e. S4 zoom, K-zoom, and galaxy camera) has not stimulated much product sale in the worldwide market. In order to understand the underlying reason in a more objective way, a TRIZ based framework is adopted to perform systematic analyses. As shown in Table 4, a couple of inventive principles are generated to look at multi-functional phone-cameras from two angles, namely, improved features and worsening features. After consulting experienced domain experts, three of them are reserved as potential solutions, including principle 1 (segmentation), principle 5 (combining), and principle 28 (replacement of a mechanical system). Here, let us use industrial examples (see Fig. 5) to explain three inventive principles. The ‘‘segmentation’’ principle (separable camera-lenses equipped with light sensitive cells but without a visible screen) has been adopted by Sony and Altek (the top 2 worldwide camera OEM/ODM manufacturer). In contrast, by incorporating an optical-zoom lens into smart phones, Samsung adopted the ‘‘combining’’ principle to produce phone-cameras. For convenience, Sony’s camera-lenses and Samsung’s phone-cameras are illustrated in Tables 5 and 6, respectively. Inspired by principle 28, a global Taiwanese EMS (Electronics Manufacturing Services) company plans to develop powerful but portable products to compete with Samsung’s existing alternatives. Owing to the reason of business secret, its name is not revealed here. In order to enhance the reliability and the validity of sent questionnaires, the invited respondents need to pass a screening process. Specifically, most of them are industry engineers who work

4. Length of a nonmoving object ? 26 4. Length of a nonmoving object ? 3, 16, 28, 32 4. Length of a nonmoving object ? 26

Table 5 TRIZ’s principle 1 – Segmentation.

CCD/CMOS sensor CCD/CMOS pixels Optical zoom Maximal aperture Wide angle Volume (mm3) Weight (g)

Sony’s QX10

Sony’s QX100

Sony’s QX30

1/2.3 inch 18.2 mega 10 3.3–5.9 25 mm 62.4  61.8  33.3 105

1.0 inch 20.2 mega 3.6 1.8–4.9 28 mm 62.5  62.5  55.5 179

1/2.3 inch 20.4 mega 30 3.5–6.3 25 mm 68.4  65.1  57.6 193

Table 6 TRIZ’s principle 5 – Combining. Product features

F1 CPU type F2 RAM capacity (GB) F3 Screen size (inch) F4 Screen resilution (ppi) F5 Thickness (mm) F6 Weight (gram) F7 Camera pixels (mega) F8 Optical zoom F9 Maximal aperture F10 Wide angle (mm)

Samsung’s Phone-cameras S4 zoom

K zoom

Galaxy camera

Dual 1.5 4.3 256 24 205 16.2 10 3.1 24

Hexa 2 4.8 306 21 200 20.7 10 3.1 24

Quad 2 4.8 308 25 300 16.2 21 2.8 23

in the Hsinchu science park of Taiwan and their demographic profiles are provided in Table 7. Actually, they are acting experienced

Sony’s QX series

Samsung’s S4 zoom

Proposed periscopic lenses (Minolta’s Dimage X t)

Fig. 5. An illustration for demonstrating varieties of phone-camera alternatives.

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234

Table 7 The basic profiles of the surveyed respondents. Demographic variables

Descriptions (in %)

Age

Below 30 (33%), 30–45 (52%), above 45 (15%) Male (70%), female (30%) Production planning (25%), quality control (21%), marketing sales (19%), product/ project executive (18%), graduate students (17%) Photo quality (51%), Visualization (21%), System performance (18%), others (10%)

Gender Occupation (job titles)

Concerning about smartphone’s functional dimension

users (focused groups) to carry out a marketing assessment. Only the invited respondents who are most concerned about the ‘‘photo’’ quality of smart phones will be kept to go through the second phase (association rule mining & conjoint analysis). In order to provide more managerial insights on assessing multi-functional varieties, this section constructs a user-driven framework to benchmark currently existing alternatives and new design concepts. 4.1. Investigating customer perceptions to identify significant product features A dichotomous scale shown in Table 8 is applied to the invited respondents to elicit their perceptions of product features and purchase intentions on Samsung’s current alternatives. Based on the aggregated user perceptions associated with decision labels (purchase intentions), association rule mining (see Eqs. 1–3) is applied to identify critical product features. In simple words, 1-itemset is utilized to seek key attributes that formulate customer dissatisfaction. And the thresholds for ‘‘confidence’’ and ‘‘coverage’’ are set as 0.7 and 0.3, respectively. According to Table 9, very interesting results are observed. Feature A5 (thickness) is seen as the most unsatisfactory among the Samsung’s three alternatives. Except for the galaxy camera, feature A9 (maximal aperture) is not perceived satisfactorily in both S4 zoom and K-zoom. As we know, one of the key characteristics for distinguishing digital cameras from smart phones is optical zoom. The other key features, such as screen size, screen resolution and RAM capacity, come from S4 zoom. In addition to identifying significant attributes that formulate customer dissatisfaction, Table 9 also responds to the trade-offs among product features. In other words, if an improved feature is tackled (like TRIZ feature 24, 28, or 37), a worsening feature needs to be concurrently considered (like TRIZ feature 2 or 4). Otherwise, design concepts may not be well accepted by end consumers and poor product sale may possibly happen in practice. 4.2. Incorporating customer preferences into the process of product validation After reviewing Table 9 again, six product features are identified as significant factors influencing users’ satisfaction, including F2 (RAM capacity), F3 (screen size), F4 (screen resilution), F5 (thickness), F6 (weight), and F9 (maximal aperture). For simplification,

Table 9 Conducting association rule mining to identify key features. Product features

F1 CPU performance F2 RAM capacity F3 Screen size F4 Screen resilution F5 Thickness F6 Weight F7 Camera pixels F8 Optical zoom F9 Maximal aperture F10 Wide angle

Samsung’s Phone-cameras S4 zoom

K zoom

Galaxy camera

⁄ ⁄ ⁄ ⁄

⁄

⁄ ⁄

⁄

⁄

Table 10 The derived weights and part-worth utilities of phone-cameras. Critical features

Specifications

Details

F2 RAM capacity

6 1.5 GB >1.5 GB

0.154

1.063 1.063

F3 Screen size

6 4.5 inch >4.5 inch

0.188

1.295 1.295

F4 Screen resolution

6 300 ppi >300 ppi

0.136

0.942 0.942

F5 Maxiamal thickness

6 1.5 cm 1.5 cm  2.0 cm >2.0 cm

0.239

1.885 0.463 1.422

F6 Weight

>200 g 6 200 g

0.107

0.742 0.742

F9 Maximal aperture

Below f = 2.8 Above f = 2.8

0.175

1.211 1.211

only F5 is divided into three levels (it is concurrently recognized as a key feature for Samsung’s S4 zoom, K zoom, and galaxy camera) while two levels are used for the remaining features. In conducting conjoint analysis, Table 10 implies that there are up to 96 (25  3) portfolios of product features required for alternative ranking. Again, the objective of employing CA is to derive the priorities of product features (see Eq. (5)) and customer utilities of associated levels for validating designed products. In Table 10, it is observed that F5 (0.239) > F3 (0.188) > F9 (0.175) consists of the sequence of the top three product features. This finding also supports the fact that aesthetic features are generally more important than functional features to influence users’ buying decisions. Based on the derived customer utilities of associated levels, Samsung’s three alternatives (i.e. S4 zoom, K zoom, and galaxy camera) coupled with the proposed idea and Asus’s Zenfone zoom can be systematically evaluated for benchmarking. In this example, inspired by TRIZ’s principle 28 (replacement with a mechanical system), a technique (periscopic lens) widely used in water-proof digital cameras (Minolta’s Dimage Xt and Sony’s TX30) is adopted in the proposed concept (A4) and Asus’s Zenfone zoom (A5). The most significant difference between Samsung’s alternatives and the periscopic lens is, when employing an optical zoom, no optical lens will stretch out and draw back through the camera frame. Thus, this mechanical design can effectively reduce the thickness

Table 8 An illustrated questionnaire to gather users’ opinions. Scales

Objects

Purchase intention

Corresponding questions  Will you consider purchasing the following Samsung’s products (multiple choices)? S4 zoom h, K zoom h, Galaxy camera h

Binary (0/1)

Phone- cameras

User satisfaction

 Are you satisfied with feature Fi for the above-mentioned alternatives?

Binary (0/1)

F1F10

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234 Table 11 An overall comparison for benchmarking various design alternatives. Product features

F2 RAM capacity F3 screen size F4 screen resolution F5 thickness F6 weight F9 maximal aperture Aggregate utility

Varieties of phone-cameras A1 – S4 zoom

A2 – K zoom

A3 – Galaxy camera

A4 – Proposed

A5 – Zenfone zoom

1.5 GB 4.3 inch 256 ppi 24 mm 205 g f = 3.1 6.675

2 GB 4.8 inch 306 ppi 20 mm 200 g f = 3.1 2.368

2 GB 4.8 inch 308 ppi 25 mm 300 g f = 2.8 2.347

2 GB 5.0 inch 336 ppi 15 mm 190 g f = 3.3 4.716

2 GB 5.5 inch 401 ppi 12 mm 185 g f = 2.7 7.138

to a degree of less than 15 mm (remember that the thickness of newly designed smart phones is usually less than 10 mm). However, due to physical limits, maximal optical zoom in a periscopic lens is limited to 3-to-5 which is much less than 10 or 21 used in Samsung’s existing alternatives. Now, to justify product validity, Table 11 demonstrates the result of assessing five benchmarking alternatives (A1–A5) in a user-driven manner. Apparently, by controlling thickness, both A4 and A5 outperform Samsung’s existing alternatives (A1–A3) in terms of maximal utility although their optical zoom are less or equal to 5. In particular, Asus’s Zenfone zoom is more preferred than the proposed concept because of its bigger aperture. Nevertheless, the proposed alternative owns a 5 optical zoom and a smaller screen size for acquiring the ad-hoc segment.

in reducing the thickness of phone-cameras while keeping a powerful optical-zoom. Finally, several research limitations are pointed as follows: (1) TRIZ is adopted to tackle hardware characteristics without considering software or firmware design (user interface), (2) in addition to optical-zoom, other product features like image sensor size (better photo quality) and front camera pixels (convenient selfie) could be included to acquire distinct segments, and (3) for better understanding different groups, market segmentation needs to be performed in advance. In future studies, the presented framework might be adaptively extended to accommodate a broader arena of business scenarios. In addition, customer co-creation, automated multi-issue negotiation (Dereli & Altun, 2012, 2013), or social media mining also deserves to be further addressed to achieve better product design and development.

5. Conclusions Despite the emerging smart phones significantly diminishes the market share of the low-end digital cameras, current phone-cameras are not as popular as originally expected. Intuitively, three questions arise from real business scenarios: (1) Are there any engineering conflicts to result in poor sale of phone-cameras? (2) Which features are critical to impact on user satisfaction? (3) How to elicit customer utilities for assessing potential varieties prior to launching? In this context, a TRIZ based framework combining association rule mining (ARM) with conjoint analysis (CA) is presented to overcome the aforementioned problems. By means of a contradiction matrix, three solutions, such as principle 1 (segmentation), principle 5 (combining) and principle 28 (replacement with a mechanical system), are suggested as potential alternatives. In general, TRIZ is a powerful tool to find inventive solutions since TRIZ can avoid human psychological inertia and break the conventional mindset. However, TRIZ is incapable to assess these inventive solutions in a systematic manner. In this paper, association rule mining and conjoint analysis are fused into the framework of TRIZ. In summary, the merits are particularly highlighted below:  Innovative phone-cameras for accommodating the trade-offs between improved features and worsening features are systematically generated via TRIZ,  Key product features that formulate customer dissatisfaction and result in poor purchase intentions (product sales) are efficiently identified via ARM,  In order to justify the validity of various design concepts, customer utilities of key features are derived via CA for accomplishing effective benchmarking. More importantly, several industrial examples are illustrated to justify the validity of the TRIZ’s solutions, such as Sony’s QX series (principle 1), Samsung’s phone-cameras (principle 5), and Asus’s Zenfone zoom (principle 28). In particular, inspired by principle 28, design by using periscopic lenses provides a promising way

References Agrawal, R., Imielinski, T., & Swarmi, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD conference on management of data (pp. 207–216). Alptekin, S. E. (2012). A fuzzy decision support system for digital camera selection based on user preferences. Expert Systems with Applications, 39(3), 3037–3047. Altshuller, G. S. (1984). Creativity as an exact science. The theory of the solution inventive problems. New York: Gordon and Breach Science Publishing. Altun, K., Dereli, T., & Baykasog˘lu, A. (2013). Development of a framework for customer co-creation in NPD through multi-issue negotiation with issue tradeoffs. Expert Systems with Applications, 40(3), 873–880. Ayag˘, Z., & Özdemir, R. G. (2009). A hybrid approach to concept selection through fuzzy analytic network process. Computers & Industrial Engineering, 56(1), 368–379. Bae, J. K., & Kim, J. (2011). Product development with data mining techniques: A case on design of digital camera. Expert Systems with Applications, 38(8), 9274–9280. Chai, K. H., Zhang, J., & Tan, K. C. (2005). A TRIZ based method for new service design. Journal of Service Research, 8(1), 48–66. Chen, J. L., & Chen, W. (2007). TRIZ based eco-innovation in design for active disassembly. In Proceedings of the 14th CIRP conference on life cycle engineering (pp. 83–87). Crawford, M., & Benedetto, A.D. (2008). New products management, 9th ed. New York: McGraw-Hill. Dereli, T., & Altun, K. (2012). Modified Even-Swaps: A novel, clear, rational and an easy-to-use mechanism for multi-issue negotiation. Computers & Industrial Engineering, 63(4), 1013–1029. Dereli, T., & Altun, K. (2013). A novel approach for assessment of candidate technologies with respect to their innovation potentials: Quick innovation intelligence process. Expert Systems with Applications, 40(3), 881–891. Fogliatto, F., Giovani, J. C., & Siverira, D. (2008). Mass customization: A method for market segmentation and choice menu design. International Journal of Production Economics, 111, 606–622. Isßıklar, G., & Büyüközkan, G. (2007). Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Computers Standards & Interfaces, 29, 265–274. Kwong, C. K., Luo, X. G., & Tang, J. F. (2011). A methodology for optimal positioning with engineering constraints consideration. International Journal of Production Economics, 132(1), 93–100. Lai, H. H., Lin, Y. C., Yeh, C. H., & Wei, C. H. (2006). User-oriented design for the optimal combination on product design. International Journal of Production Economics, 100, 253–267. Luce, R. D., & Turkey, J. W. (1964). Simultaneous conjoint measurement. A new type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27. Lilien, G.L., & Rangaswamy, A. (2003). Marketing engineering-computer assisted marketing analysis and planning, 2nd ed. New York: Prentice Hall.

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C.-H. Wang / Computers & Industrial Engineering 85 (2015) 227–234

Ries, E. (2011). The lean startup: How today’s entrepreneurs use continuous innovation to create radically successful businesses. New York: Crown Business. Sheu, D. D., & Hou, C. T. (2013). TRIZ-based trimming for process-machine improvements: Slit-valve innovative redesign. Computers & Industrial Engineering, 66(3). 555-266. Su, C. T., & Lin, C. S. (2008). A case study on the application of fuzzy QFD in TRIZ for service quality improvement. Quality & Quantity, 42(5), 563–578. Wang, C. H. (2015). Integrating Kansei engineering with conjoint analysis to fulfill market segmentation and product customization for digital cameras. International Journal of Production Research, 53(8), 2427–2438. Wang, C. H., & Chen, J. N. (2012). Using quality function deployment for collaborative product design and optimal selection of module mix. Computers & Industrial Engineering, 63(4), 1030–1037. Wang, C. H., & Wang, R. D. (2014). Combining fuzzy AHP and fuzzy Kano to optimize product varieties for smart cameras: A zero-one integer programming perspective. Applied Soft Computing, 22, 410–416.

Wang, C. H., & Wu, C. W. (2014). Combining conjoint analysis with Kano model to optimize product varieties of smart phones: A VIKOR perspective. Journal of Industrial and Production Engineering, 31(4), 177–186. Yamashina, H., Ito, T., & Kawada, H. (2002). Innovative product development process by integrating QFD and TRIZ. International Journal of Production Research, 40(5), 1031–1050. Yang, C. C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers & Industrial Engineering, 59, 682–689. Yeh, C. H., Huang, C. Y., & Yu, C. K. (2011). Integration of four-phase QFD and TRIZ in product R&D: a notebook case study. Research in Engineering Design, 22(3), 125–141. Zhang, F., Yang, M., & Liu, W. (2014). Using integrated quality function deployment and theory of innovation problem solving for ergonomic product design. Computers & Industrial Engineering, 76, 60–74.