A data-mining approach for product conceptualization in a web-based architecture
Introduction
Few companies, especially small- and medium-sized enterprises, now possess sufficient expertise or proficiency to develop a complete product [1]. Nevertheless, companies can gain more control in their competitive arena by cooperating with other companies. Furthermore, enterprises are recognizing that they must devote more effort to product conceptualization rather than later stages of the new product development (NPD) life cycle, because of its disproportionate impact on the final product. Accordingly, it is crucial to improve design consistency yet manage design conflict amongst design participants. To this end, Pahng et al. [2] integrated designer-specified mathematical models for multi-disciplinary and multi-objective design problems. Alternatively, Lu et al. [3] analyzed the relationship between design process and design conflict, and thereafter developed a framework in terms of technical and social factors.
Rapid advancing information technology (IT) has increased the possibilities for product conceptualization and the importance of its role in NPD. This is further enhanced if the companies can use ITs to form an alliance for the purposes of in-depth technical and business process integration [4]. Technologies for product design have been frequently explored and include: a standard for the exchange of product model data (STEP) translation [5]; database management systems (DBMS) (e.g. [6]); real-time 3D CAD systems (e.g. [7]); and virtual reality modelling language (VRML) displays [8]. On the other hand, to enhance the ability of product conceptualization, rather than individual capability alone, research work has focused on communication and coordination amongst distributed resources, e.g., knowledge-based system (KBS) [9], design management system (DMS) [10], and conceptual design tool [11].
Thus, all these methodologies have emphasized embodiment design, rather than product conceptualization, so that exploitation of early design creativity and efficiency has not been fully explored. Accordingly, there still exist a number of critical issues in product conceptualization. As such, product concept development must perform a number of complex functions with respect to design methodology, concurrency, teamwork, knowledge management and design representation [12]. In so doing, the IT realization of conceptualization systems is likely to be a major problem. In this regard, the data-mining technology presents a logical alternative.
In recent years, data mining has been increasingly advocated in academia and industries. Its applications are widespread in such disciplines as marketing [13], engineering [14], biology [15], and web analysis (i.e., web mining) [16]. Specifically for product development, a number of research efforts were attempted in product data management (PDM), the scope of which has evolved from the internal efficiency of a company into the incorporation of both internal and external issues [17]. In the past few years, the fundamental paradigm shift in data mining for product development has emphasized on improving outward-facing activities in an organization, such as electronics data interchange (EDI) [18], customer relationship management (CRM) [19], enterprise resource planning (ERP) [20], virtual enterprise (VE) [21], supply chain planning (SCP) [22], and Internet-based commerce (IBC) [23].
However, some issues have not been well addressed in the previous work, such as lacking of quantitative analysis methods, low knowledge transparency, extensibility and predictability, and scarcity of effective customer management. To deal with these problems, some researchers recognized the importance of knowledge-based data-mining approaches, such as feature space theory [24], knowledge refinement [25] and rule-based classification [26]. Furthermore, in NPD, the product development team should incorporate customer concerns into product concepts. This may bring a significant benefit to the company because of higher customer satisfaction to the product, for example, CRM-based methodology [13] and web-mining model [27].
Nevertheless, there are two major omissions in existing work: first, a unified framework in the process of product conceptualization, which integrates customer requirements with design knowledge management in the early stages of product development, has not been well addressed; and second, it is imperative to attain an effective data-mining approach to support the product conceptualizing process. Based on this understanding, an axiomatic product conceptualization system (APCS) has been developed to meet the demand of product concept development. The proposed APCS comprises three cohesively interacting modules: namely, knowledge elicitation module using laddering technique; knowledge representation module using design knowledge hierarchy (DKH); and knowledge synthesis module using restricted Coulomb energy (RCE) neural network. Accordingly, this system offers a method of making design decisions via a web-based data-mining product conceptualization approach. A case study on wood golf club design has been used to illustrate and validate the system. The details of the validation are discussed.
Section snippets
Background
Axiomatic design has gained wide attention in recent years. To date, many researchers have attempted applying axiomatic design theory to such disciplines as product design [28], software system design [29], mechanical system design [30], manufacturing system design [31], design for environment [32], and design for ergonomics [33]. Moreover, owing to axiomatic design is a general theoretical framework, rather than a specific modelling methodology, it has frequently been integrated with other
Data-mining technology for axiomatic design
Referring to Fig. 1, for the purpose of product conceptualization, such knowledge carriers as customers, designers and domain experts could be concurrently involved in the APCS. This aims to elicit CAs/FRs using such tool as the laddering technique that is handled by domain experts. As stated by Pyle [45], data mining, which was originated from classical statistics, is associated with a process of analyzing data from various sources and soliciting hidden patterns or trends within them by means
Application of a web-based data-mining system
The web-enabled APCS is established for organizations to meet the demand of an efficient and flexible strategy for axiomatic product conceptualization in the early product conceptualizing stage. In the prototype system, the geographically distributed designers and customers can use the web site as a shared platform. The data-mining applications of the web-enabled APCS are shown in Fig. 5. The fundamental components required to set up the web environment include active server pages (ASP),
Concluding remarks
This approach has revealed the potential of improving conventional axiomatic design theory in terms of effective product concept development. For this purpose, a prototype axiomatic product conceptualization system (APCS) has been established. Compared with previous axiomatic-design-related approaches, it possesses the following strengths:
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The customer’s and designer’s knowledge, which is used as the inputs to CAs/FRs mapping, can be genuinely elicited using a psychology-originated technique,
Acknowledgements
The research was supported by Shanghai Education Committee Research Project (Project No. 07SG52), Shanghai Science & Technology Committee NSF Project (Project No. 08ZR1409200), and National High-Tech R & D Plan (Project No. 2007AA04Z105).
Wei Yan is an associate professor in the Logistics Engineering School at Shanghai Maritime University, China. His research work is oriented to customer-oriented product concept development, knowledge management and artificial intelligence, supply chain management and logistics engineering. He has a BEng degree in mechanical engineering from Shanghai Jiao Tong University, China, an MEng dgree in mechanical engineering from National University of Singapore, and a PhD degree in mechanical and
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Wei Yan is an associate professor in the Logistics Engineering School at Shanghai Maritime University, China. His research work is oriented to customer-oriented product concept development, knowledge management and artificial intelligence, supply chain management and logistics engineering. He has a BEng degree in mechanical engineering from Shanghai Jiao Tong University, China, an MEng dgree in mechanical engineering from National University of Singapore, and a PhD degree in mechanical and production engineering from Nanyang Technological University, Singapore. He has several years of engineering experience in industry prior to his graduate study. He is also an editorial board member of the journal Transactions of Chinese Construction Engineering Association.
Chun-Hsien Chen is an Associate Professor in the School of Mechanical & Aerospace Engineering at Nanyang Technological University, Singapore. He received his BS degree in Industrial Design from National Cheng Kung University, Taiwan, MS and Ph.D. degrees in Industrial Engineering from the University of Missouri-Columbia, USA. He has several years of product design & development experience in industry. His research interests are in collaborative/consumer-oriented product development, knowledge management for design and manufacturing, and artificial intelligence in product/engineering design. He is an editorial board member of the journals Advanced Engineering Informatics and Recent Patents in Engineering.
Youfang Huang is a professor, the director of Logistics Research Center, the Vice President at Shanghai Maritime University, China. His research work is focused on logistics management and engineering. He has a BEng degree in mechanical engineering from Shanghai Maritime University, an MEng dgree and a PhD degree in mechanical engineering from Tongji University in Shanghai, China. He is also an international consultant of UNCTAD/WTO, the chairman of Logistics Association in Northeast Asia, and the deputy president of Chinese Logistics Association.
Weijian Mi is a professor and the Dean of Logistics Engineering School at Shanghai Maritime University, China. He has a BEng degree in mechanical engineering from Shanghai Maritime University, an MEng dgree and a PhD degree in mechanical engineering from Tongji University in Shanghai, China. He is also a committee member of the Chinese Logistics Association and Chinese Construction Engineering Association. His research and teaching interests are involved in large-scaled container equipment and port information system design and implementation.