A Scheme for Online Customized Product Design by Co. SEP

Studies in System Science (SSS) Volume 1 Issue 2, June 2013

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A Scheme for Online Customized Product Design Huei-Chen Hsu*, Hui-Chun Chan, Ya-Li Chung Department of Business Administration, Department of Marketing Management, TransWorld University, Yunlin, Taiwan maggie@twu.edu.tw; chwen@twu.edu.tw; yali@twu.edu.tw Abstract In this research, two different theoretical models are used for online customized product design to establish the relationship of product evaluation between status of customer demands and the features of the product. Based on the extant theories and the database of the product modules, a classification scheme has been developed composed of two models: (1) Product design model I: the use of Fuzzy information axiom as the evaluation and decision principle of the product design model. (2) Product design model II: the Analytical Hierarchy Process, Neural Network, Fuzzy set theory and Gray Relational Analysis are used for the evaluation of the product design model. This study provides evidence that two companies can use the system to recommend suitable modulated product according to the needs of different customers. Similarly, the customers can use the system to search the desired products by inputting the requirement information. Implications of the research are suggested for academics and practitioners.

customized products and how they behave across different purchase scenarios and that the implications for the variations in customer behaviour across product types is at the heart to manage e-business effectively. Due to China’s online market gains momentum in recent years; some forecasts suggest that China’s online market will overtake the U.S online market by the year 2015 [Hsiao and Huang, 2002]. This increased income, especially in urban areas, and the increasing choice of available products as a result of the continuous opening up of the economy, have led to rapid growth in retail consumption, during 2000 to 2011 at an average annual rate of 20.4 precent. Retail sales of consumer goods in 2011 have reached U. S. $ 537 billion [Chinese Statistical Yearbook, 2011].

Online business environments result in problems associated with insecurity and privacy among transaction counterparts, which puts pressure on Internet marketers to create a trust that is much stronger and more persistent than what, is normally demanded offline [Hsiao and Huang, 2002]. In commodity market, customers can purchase products with different functional level according to the conditions of their demands.

This approach ensures that enterprises not only attain higher sales amounts and profit space, but it will also be a key factor in industrial development of the online generation [Tsai, Juang, Jaw, and Chen, 2006]. Therefore, in the product design process, we need to help consumers select the right goods. In the past research, greater emphasis was put on the design of products from the standpoint of product information for decision-makers [Verma, Plaschka, Hanlon, Livingston and Kalcher, 2008]. Each consumer’s degree of demand for a product is viewed as equal in importance, but the outcome cannot be equally reached. Nevertheless, there are various degrees of relations between demands and product functional modules.

For example, while computers and books are both products sold on the Internet, it is conceivable that the customization practices valued by customers in the purchase of a computer may be different from those in the purchase of groceries. Recognizing that a vital key to retain online customers is to understand what they want, how different consumer groups products of

The empirical analysis for this study is based on two different theoretical models and a step toward addressing such needs for customized module design has been made. This research is focused on addressing the following questions: (1) how is the functional association built up between the evaluations between consumer demand and product characteristics? (2)

Keywords Customization; Analytical Hierarchy Process; Fuzzy Set Hybrid

Introduction

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And, how to present a customer business referral service system? China is now the world’s largest manufacturing base as well as the world’s market. Accordingly, the study examines Chinese online customer value for customization across the product types. Definition on how to help consumers buy products, based on specific recommendations for producers and consumers, is a major area under refinement. Literature Review Innovation in Online Product Designs Technologies Product designs in electronic market—featured with many key factors to be considered for academic research and theory—have been achieved through a number of key items. Technologies nowadays enable firms to provide digital content and services customized to individuals on the basis of knowledge about their preferences and behaviour. Ansari and Mela [Ansari and Mela,2003] suggest that the digitization and networking capabilities enable a variety of customization approaches that make the Web site more appealing to the customers in order to increase traffic. In this study, customized modules are designed taking into account the following key items: (1) customer needs, (2) functional characteristics, (3) database, (4) evaluation and decision theory, and (5) optimum product search. Linking these five key factors will have a great influence on the product designs, as well as the product features and product designers. Owing to the emergence of the concept of supply chain management, customization strategy has attracted more and more attention. The customer need identify for the module, such style also has to be sold to consumers, and therefore, a lot of demands squash into the product database. The best evaluation and decision theory will be selected by the product designer to achieve the final goal and the ultimate optimum product will be searched. Product Designs and Online Customer Purchase Behaviour To effectively manage electronic retailing, it is necessary to understand the nature of the product type, the associated purchase behaviour, and the applicability of customization strategies to the product type [Clark and Lyons, 1999]. Customers purchasing these products on the Internet will be looking for a

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strong attachment to their own vision and values. Customers would be receptive to having a product with these characteristics with a long product life cycle and customization. Hence, it can be expected that product consumption by consumer is more than subjective self-awareness. The consumer purchasing behaviour result changes in product design and the design intent. The unit value of the product and the significance of each individual purchase are high. Consumers are used to employing adjectives to describe the demand for the product level, but the semantics of expression is full of considerable degree of fuzzy logic and uncertainty. Hsiao and Huang [Thirumalai and Sinha, 2009] used the computer-aided design and neural networks to assist in product design. Using fuzzy theory principles of information to design the best product seems to be a simple, efficient method, and if only the expression of consumer demand semantics can be used to quantify the fuzzy theory. However, what if the characteristics of functional elements are not completed? In such case, the customized combination of modules can be obtained using the AHP to rank the importance of customer needs in order to get a back propagation neural network to obtain the characteristic function of the importance and the gray relational analysis. This research approaches the best combination of features for the modular product features. Research Model Fuzzy Theory Neural networks imitate human nervous conveyor systems to emulate a supervised learning neural network. The network structure can be divided into an input layer, a hidden layer, and an output layer. The theorem uses the non-linear to reflect relation of input and output, which is used to calculate the appropriate network weights value and bias, so that it can achieve the result of the output reflection in the range of tolerance error. The TFN can be displayed as:

0,  x-t  1  t 2 -t1 μ(x)=   x-t 3  t 2 -t 3  0,

(1)

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Fuzzy Information Axiom (FIA)

TABLE 1 COMPARISON RATE OF CUSTOMER’S DEMAND DEGREE

The FIA was proposed by Hsiao and Huang (2002) from the Massachusetts Institute of Technology, further developed QFD (Quality Function Development) and the Axiomatic Design (AD). The main purpose of FIA is to produce a simple design. Hsiao and Huang defined the information content as IC (Information Content) which is calculated using the formula IC= log (design range/common range). If the IC is small, implying that there is a large common range and it is easier for the product of the design parameter to be successful. When the common range is larger, there is a higher possibility for manufacturers to produce a product according to the designer’s concept. The axiom of the AD is the information axiom, which states that among those designs that satisfy the independence axiom, the one with the smallest information content is the best design. The definition of information content (Ij), expressed in terms of the TFNs, is as follows: Ij = log2(1/pj)

(2)

Where pj is the ratio of the area of the common range to the area of the system range for the jth design requirement, which is also the probability of the system range meeting the design requirement. pj = [ CommomRange/SystemRange]

(3)

Assuming that a product has m number of design requirements, the summation of all the design requirement is such that: m

Itotal =

∑ Ij

(4)

j =1

The Gray Theory ) (1 / n − 1) × ϒ ( Xi , Xj= n −1 (5) ∑ ( minmin∆ij + ρ maxmax∆ij ) /(| xi ( k ) − xj ( k ) | + ρ maxmax∆ij ) k =i j k j k j k

ρ =

is a resolution factor, and generally the value is 0.5;

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∆ij

| xi ( k ) − xj ( k ) | .

Analytic Hierarchy Process (AHP) The study uses the Analytic Hierarchy Process (AHP) [Ramanathan and Ganesh, 1995] to structure the class weight of customer requirements. By means of a 9 points scale to compare the “importance degree” of the demands according to the customer’s demand degree, TABLE 1 shows the comparison rate of customer’s demand degree.

aij

Comparison rate of i-demand and j-demand

1 3 5 7 9 2,4,6,8

i-demand and j-demand are equal important i-demand is a bit more important than j-demand i-demand is more important than j-demand i-demand is much more important than j-demand i-demand is extremely more important than j-demand Intermediate values of the adjacent scale

If there are n items of basic demands, after the comparison among the customers, an n x n square matrix may be seen as Anxn. In order to ensure consistency while the paired comparison is in construction, a consistency test is performed. The consistency ratio (CR) is used to check whether the matrix is a consistent matrix. In case of CR ≤ 0.1, the paired comparison matrix features a high level of consistency. Customized Modular Product Design Model As previous researches in the module design process [Thirumalai and Sinha, 2009], the process is set up into two different theoretical models in this paper. Model I: By using Fuzzy Information Axiom (FIA) as the evaluation and design principle of the design model of product. The model theory of the FIA is the evaluation criteria considered when the concept of the product design is evaluated. The steps of the design process are described as follows. (1) Triangular Fuzzy requirement levels

number

(TFN)

for

the

(2) The relationship between the evaluation of functional requirement options and product features is examined, in order to establish rules to relate the evaluations of customers and designers. The function requirements of customers are usually hard to express explicitly, and for a designer, the requirements contain not only the main product components, but also customers’ potential needs. Therefore, fuzzy numbers are used to determine a customer need which is expressed as TABLE 2. TABLE 2 TFNS WITH SEVEN REQUIREMENT LEVELS

Vocabulary

Denotation

TFNs

A (very low) B (low) C (medium low) D (medium) E (medium high) F (high) G (very high)

VL L ML M MH H VH

(0,0,1) (0,1,3) (1,3,5) (3,5,7) (5,7,9) (7,9,10) (9,10,10)

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Studies in System Science (SSS) Volume 1 Issue 2, June 2013

The formula for the best selection product is exhibited as following: (1) The customer’s functional requirements have been assumed to calculate the median of customers’ functional requirements. If there are n basic customer needs which are translated into 7 levels of triangular fuzzy numbers, the median of customer needs is calculated and normalized to obtain the customer needs in the hierarchical order wi, as shown in formula (5). n

(5)

∑ wi = 1 i =1

(2) Computation of the requirement level for a product feature The TFNs for the relationship matrix of customer

 . needs and product features assessed by experts are A

[ ]

(6)

 A = aij

Where i and j denote ith customer need and jth product feature. The product of the relationships among the normalized customer requirement levels, weighted customer needs, and product features will give rise to

Model II: By using Fuzzy Theory, AHP, and Gray Relational Analysis, the evaluation and the product design model are designed to meet the principle. The principle of the design process is described as follows. (1) Establish an evaluation rule for the functional modules This stage is intended to build up the relation and evaluation mode between “customer demand” and “functional module” in the following three steps. Step1. Ranking and development of customer demand If there are n items of a basic customer demand, after the paired comparison is conducted, one can use the Formula--- ( A − λ I ) w = 0 to obtain the eigenvalue (w) of matrix A. Next, it is normalized, and the class sequences w*(w1*, w2*…, wn*) are obtained for customer demand and TFNs individually, which is shown as TABLE 3. TABLE3 TFNS SHOWING FIVE REQUIREMENT LEVELS

Lexical Category A(Fairly unimportant) B(Unimportant) C(Normal) D(Important) E(Fairly important)

a jth TFN for product feature bj .

bj =

∑ w x [ a ] i

(7)

i =1

(3) Computation of information content Ij Based on the results in step (2), the information content Ij of the jth product feature is determined: (8)

Ij = log 2 (1 / pj )

(4) Establishment of rules for the selection of the best product According to the fuzzy reasoning principles of feature requirements, the median of various product features is calculated, and the normalized weight of the jth product feature is w

, where ∑ w j =1

j = 1.

(5) Multiplied the information content obtained sequentially from the standardized level to acquire the grand total value for product information. E = min

m nd ∑ ( Ij × w j ) j =1

(9)

(6) Establishment of the ideal product purchasing interface for customers Customers can use this interface to select their personal needs and the system will suggest the most suitable merchandise or recommend products.

TFNs (0,0,3) (1,3,5) (3,5,7) (5,7,9) (7,10,10)

(2) Establish the relevance of “customer demand” and “functional module” The relevance between “customer demand’ and “functional module” should be established in accordance with different customer demands. Therefore, one can repeat the step1, where the fuzzy correlation is in five grades of TFNs. (3) Evaluate fuzzy lexical functional module According to previous two steps, one can follow the fuzzy logic structure “if …then” to evaluate the demand level (fairly high, high, normal, low, fairly low) of the modular function. Then, 25 fuzzy inference rules can be obtained from the interaction pairs. The 25 fuzzy inferences are shown in TABLE 4. TABLE4 CORRELATION FOR CUSTOMER DEMANDS AND FUNCTIONAL MODULES

Customer Needs VH H MH M ML L VL

Relationship Between Customer Needs and Product Features VH H MH M ML L VL VH VH H VH H H H H MH H MH MH MH MH M MH M M M M M

M M M M M M M

L L ML ML M M M

VL L L ML ML M M

VL VL L L ML ML M

Studies in System Science (SSS) Volume 1 Issue 2, June 2013

1 ∫0 f ( x ) xdx * P ij = 1 ∫0 f ( x ) dx

(10)

n * * * * P ij = ( ∑ P ij.W i ) / ∑ w j i =1 j

(11)

(4) Use the Gray Relational Analysis Model The membership can be defined that is obtained from Formula (10) an object sequence Po (p1, p2, pn). If the functional module can be reasonably assumed to be pairs, or the manufactures have set the matching methods at y combinations, one can regarded those combinations as y reference sequences Pko (p1, p2,…pn), and k = 1,2,…,y. Different gray correlations of reference sequences and object sequences through Formula (5) can be attained.

The maximum value γ max , the sequence corresponding to

γ max

, is the best suggestion on the product

functional module style according to each customer’s personal demand. Case Study Using the modules developed in our study, a modular product in design model I and model II has been measured, respectively, for the most suitable customization products for their customers. The case companies will be used for actual calculation, each of which can use the results updated on the company web in accordance with customer’s needs for the suitable module. The two companies located at Tainan city, Taiwan. One is a sales-based computer company, and the other

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is a baby-stroller manufacturing company. These two companies are compared with the calculation of modular design model I and II, each of which can use the results to promote the modular design. The First Company We have consulted two professional staff with 5 years sale experience for customer needs and important product features, whose selection of product features are listed as TABLE 5. TABLE5 CUSTOMER’S NEEDS AND PRODUCT FEATURES

Functional Needs Price Computation Speed Portability Word processing Professional Graphics Color

Serial Number CN1 CN2 CN3 CN4 CN5 CN6

Product Features Price CPU RAM HD Capacity Screen Size Display Card Color

Serial Number PF1 PF2 PF3 PF4 PF5 PF6 PF7

Suppose c customer named Jack (fabricated name) who wants to buy a laptop, but knows nothing about computer. In this case, the model I algorithm is used to help him based on his actual needs which are listed in TABLE6. TABLE6 JACK’S REQUIREMENTS FOR LAPTOP

Serial Number CN1 CN2 CN3 CN4 CN5 CN6

Functional Needs Price Computation Speed Portability Word processing Professional Graphics Color

Jack’s Requirement Level Below TWD 30,000 M MH H M Black

FIG. 1 LAPTOP CUSTOMER NEEDS INTERFACE

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The laptop customer needs interface is shown as FIG. 1. The information content indicates that the top three laptops closer to jack’s needs are “NO.1 HP-NB11, NO.2 Acer NB23 and No.3 Dell NB42”. The Second Company The setting functional classification of baby stroller is shown as TABLE 7. The modular style having the same function can follow the low-to high, equally divided into between the value (0, 1). Table 7 shows the value of relationship between customer demands and functional modules. The customer demands according to the product features are: (1) N1-sitting comfortable for baby, (2) N2-collapsibility, (3) N3-portability, (4) N4-operational usage, (5) N5-additional components (toys, tray), and (6) N6-lower price. The baby stroller purchasing interface is provided as FIG. 2.

TABLE 7 Modular function for baby stroller Item

Product Features

Folding Operation

Self-standing after folding

Reversible handle

F4 F5 F6

Accessories in front seat Seat back adjustability Foot rest

F7 Wheel F8 Suspension

Alternatives One hand +Joint pull/ Hook pull + Joint pull Joint pull Included/ not included Included /not included Tray +Toy bar/ Tray/ Bumper Multi-positions Two-positions Adjustable/ Fixed Pivoted(6 or 8 wheels)/ Fixed direction(4 wheels) Cantilever-Type Simple-Type/ None

Membership Grade 1 0.5 0 1 0 1 0 1 0.5 0 1 0 1 0 1 0 1 0.5 0

FIG. 2 THE BABY STROLLER PRODUCT PURCHASING INTERFACE

Finally, the best module suggestion on baby stroller is: (1) hook pull + joint pull, (2) self-standing after folding not included, (3) reversible handle, (4) tray + toy bar, (5) seat back adjustability (multi-position), (6) adjustable foot rest, (7) fixed direction wheel (4 wheels), and (8) cantilever-style suspension.

Conclusions Summary of Results In this study, the aim is to explore customized module design, model-building and develop an understanding of customer assistance in two case studies. The

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contributions of this paper are as follows: (1) For customized module design, to establish design patterns, the paper starts with the preference of the customer’s needs, and attends to customer purchase behaviour. (2) In model I, the FIA is designed to evaluate the product design process, and enhances customer product satisfaction taking into consideration customer’s expectation on the product quality characteristics and the assumption that customers can buy more products based on similar module choices. (3) In model II, by using Fuzzy Theory, AHP, and Gray Relational Analysis, the evaluation and the product design model are designed to match the actual customer purchasing decision in market comparison. The different results may be helpful for a company’s R&D, marketing development strategies. The way successful in business is featured with quick assess and response to meet customer’s demand in a customer-oriented era. Blurring the customer needs for a reasonable theory is a mapping of the transition to product characteristics. Sales personnel can provide assistance, engaging in various discussions in which both sides should be inspired during the process involving in co-operation. Limitations The major limitation of our study concerns our measurement approach. Customer demands for products and different levels of semantics are not yet clear. In addition, only a snapshot of two customized module design framework is available. Our dilemma is that to test the hypothesized model we need to sample a large number in equivalent way. Finally, the range of products considered in this study excludes all kinds of products. For example, while the discussion in this article is focused on provider driven personalization efforts, digital products approaches where other firms and customers can play a key role in the customization design of a provider. Implications: Theoretical and Practical

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customer needs, and identifying the important functional characteristics of modular products and components, to evaluate the relationship between the both. The modules make the decision to choose the best products, and the establishment of the universal module products as well as enterprise can then be selected based on the desired situation and there is a basis for a calculation process to build a product recommendation service system. Future Research For future research is needed to examine how antecedents and relationships of customization are evolved as consumer’ needs. Further, while the empirical setting of this study is electronic retailing via Web sites, it must be realized that technological advances today enable personalized applications across a variety of interfaces. For instance, with the availability of technologies such as global positioning systems (GPS) on mobile phones, the potential for personalized services (i.e., right content to the right customer at the right time and place) is enormous. Overall, while e-commerce technologies have the potential to transform provider–customer interactions, understanding what customers want, how they behave, and how providers can successfully make full use of the technologies to provide customer value in these settings would be fruitful lines of future inquiries. REFERENCES

Ansari, C. and F. Mela. “E-customization,” Journal of Marketing Research 40(2) (2003): 131–145. Clark, R. and C. Lyons. “Using Web-based training wisely,” Training 36(7) (1999): 51-61. Chinese Statistical Yearbook, China Statistical Publishing House, China: Beijing, 2011. Hsiao, S. and J. Huang. “A neural network based approach for product form design, “Design Studies 23 (2002): 67-84.

This key implication of this study sheds some light on this issue by showing that model I is the axiomatic evaluation of fuzzy information and decision-making rules. Model II involves the level of analysis, fuzzy theory, neural and gray relational analysis implied as evaluation and decision-making rules.

Ramanathan, R. and L.S. Ganesh. “Using AHP for Resource

The customized module design framework is an established calculus, starting from the setting of

integrative model and empirical evidence,” Psychology

Allocation Problems,” European Journal of Operation Research 80(1995):410-417. Tsai, H., H. Juang, Y. Jaw, and W. Chen. ”Why Online Customers Remain with a Particular e-retailer: an and Marketing 23(5) (2006): 447-464.

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Thirumalai, S. and K. K. Sinha. “Customization strategies in electronic retailing: Implications of customer purchase behavior,” Decision Sciences 40(1) (2009): 5-36. Verma, R., G.R. Plaschka, B. Hanlon, A. Livingston, and K. Kalcher. “Predicting customer choice in services using discrete choice analysis,” IBM Systems Journal 47(1) (2008):179–191. Hui-Chun Chan is a lecturer in the Department of Business Administration of TransWorld University, Taiwan. Her research interest is in the area of Information Management in

Studies in System Science (SSS) Volume 1 Issue 2, June 2013

computer. Ya-Li Chung is a lecturer in the Department of Business Administration of TransWorld University, Taiwan. Her research interest is in the area of Business Administration. Huei-Chen Hsu received the PhD degree in marketing management from National Yunlin University, Yunlin, Taiwan, in 2008. Since 2012, she has been a professor in the Department of Marketing Management and as a director of Center for Computer & Information Service in 2010 at TransWorld University where she instructs communication networks. Her research activity focuses on consumer behavior and network communication.