Evaluating new product development performance by fuzzy linguistic computing Evaluating new product development performance by fuzzy linguistic computing Wen-Pai Wang * Department of Industrial Engineering and Management, National Chin-Yi University of Technology, 35, Lane 215, Section 1, Chung-Shan Road, Taiping City, Taichung 411, Taiwan, ROC a r t i c l e i n f o Keywords: New product development Group decision making Heterogeneous information 2-Tuple fuzzy linguistic computing a b s t r a c t New product development (NPD) is indeed the cornerstone for companies to maintain and enhance the competitive edge. However, developing new products is a complex and risky decision-making process. It involves a search of the environment for opportunities, the generation of project options, and the evalu- ation by different experts of multiple attributes, both qualitative and quantitative. To perceive and to measure effectively the capability of NPD are real challenging tasks for business managers. This paper presents a 2-tuple fuzzy linguistic computing approach to deal with heterogeneous information and information loss problems during the processes of subjective evaluation integration. The proposed method which is based on the group decision-making scenario to assist business managers to measure the performance of NPD manipulates the heterogeneous integration processes and avoids the informa- tion loss effectively. Finally, its feasibility is demonstrated by the result of NPD performance evaluation for a high-technology company in Taiwan. � 2009 Elsevier Ltd. All rights reserved. 1. Introduction Product design has been long recognized as an opportunity for differential advantage in the market place. A number of companies successfully focus on product design as a competitive tool (Creusen & Schoormans, 2005). Nowadays, more requirements for enter- prises have been put forward, such as more product variety, short- er time-to-market, lower product cost and higher quality. The globalization of competition in the manufacturing industry and the diversification of customers’ demands as well as rapid techno- logical developments continue to spur technology-based innova- tions at a frenetic pace. Product design innovation therefore has developed quickly and has gradually become one of mainstream production modes of manufacturing industries in the 21st century. Therefore, improving product development performance is becom- ing increasingly important and challenging. New product development (NPD) is undeniably vital in deter- mining the economic success of manufacturing companies. Firms need to create and sustain competitive advantages in order to sur- vive in today’s highly competitive business environment. One ma- jor determinant of sustaining competitive advantage is the ability of the firms to develop and launch successful new products. Differ- entiation through NPD is therefore one of the most effective strat- egies for achieving success. As competition in global markets has become intense, firms have begun to recognize the importance of NPD and innovation issues. Through innovation and the introduc- tion of new products, new markets and growth possibilities can be created. Increasing international competition accentuates the importance of the NPD process which is secure and accurate (Ozer, 2005; Sherman, Berkowitz, & Souder, 2005). Gemser and Leenders (2001) conclude that being innovative with respect to design and design strategy can enhance competitiveness regardless of indus- try evolution. Timely, correct and responsive NPD has become even more critical in the highly competitive global environment. The need to respond quickly to these dynamic global market forces re- quires the firm to establish a specialized evaluation mechanism and platform for NPD performance. However, the decision-making domain of NPD is highly com- plex and uncertain due to a demanding environment characterized by increased globalization and segmentation of markets, increased levels of product complexity, changing customer needs, and short- er product life cycles (Belecheanu, Pawar, Barson, Bredehorst, & Weber, 2003). New product introduction in today’s technology-dri- ven markets carries significant risk. New product failure rates can be as low as one of every three products or as high as the 90% of new grocery products which are withdrawn within a year of their introduction. New technology, improved communications, in- creased profit demands and shorter product life cycles have added to the inherent risk. Yet, without the introduction of new products, deterioration of the firm’s market position is inevitable. Without new products, firms will inevitably stagnate (Yelkur & Herbig, 1996). In order to evaluate the performance of NPD more appropri- ately, the firms should consider not only quantitative index but also qualitative dimensions or factors which are evaluated by multiple decision-makers or experts. Thus, the evaluation of NPD performance should be regarded as a group multiple criteria decision-making problem as well. 0957-4174/$ - see front matter � 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.02.034 * Tel.: +886 4 23924505x6018; fax: +886 4 23921742. E-mail address: wangwp@ncut.edu.tw Expert Systems with Applications 36 (2009) 9759–9766 Contents lists available at ScienceDirect Expert Systems with Applications j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a mailto:wangwp@ncut.edu.tw http://www.sciencedirect.com/science/journal/09574174 http://www.elsevier.com/locate/eswa Experts devote themselves to judge the NPD performance mea- surement by their experiential cognition and subjective perception in the decision-making process. However, there exists a consider- able extent of uncertainty, fuzziness and heterogeneity (Hwang & Yoon, 1981). This is not a seldom situation. In addition, it is prone to information loss happening during the integration processes, and gives rise to the evaluation result of the performance level which may not be consistent with the expectation of the evalua- tors. Consequently, developing an easy way to calculate the perfor- mance ratings while the processes of evaluation integration and to manipulate the operation of qualitative factors and expert judg- ment appropriately in the evaluation process of NPD could brook no delay. In this paper we propose a suitable model based on 2-tu- ple fuzzy linguistic information to evaluate the NPD performance. The proposed approach not only inherits the existing characters of fuzzy linguistic assessment but also overcomes the problems of information loss of other fuzzy linguistic approaches (Herrera- Viedma, Herrera, Martinez, Herrera, & Lopez, 2004). This paper is organized as follows. In Section 2 the measure- ment dimensions of NPD are described. In Section 3 we introduce the basic definitions and notations of the fuzzy number, linguistic variable and 2-tuple fuzzy linguistic representation and operation, respectively. In Section 4a NPD performance measurement meth- od based on 2-tuple fuzzy linguistic information is proposed . The proposed model is then illustrated with an example for a high-technology company in Taiwan. In Section 5 conclusion is given. 2. Literature review A contemporary NPD process usually consists of hundreds or thousands of activities, where the activities may be dependent or interdependent on one another. A rapidly changing competitive landscape and dynamic customer expectations require manufac- turers to seek flexibility in product development. Unlike the man- ufacturing processes, product development is a creative and discovering process that tends to create something new from trial-and-error and learning from the errors made (Wang & Lin, forthcoming). The purpose of NPD is to accumulate the knowledge and capability necessary to determine an appropriate new product. Superior product design, potential for breakthrough innovation, low project and product cost, shorter lead time, better communica- tion of cross-functional teamwork, and increased customer satis- faction and market share are among many other advantages for successful NPD. Suchlike concerns enable firms in making NPD decisions while ensuring full knowledge of the customer, the tech- nology, and with the team’s support. In view of this, a performance evaluation method or approach that is capable of systematically analyzing and accurately quantifying those subjective experiences and judgments of the NPD team is highly required. Ozer (2005) indicated that the quality of new product evalua- tion decisions is affected by four major sets of factors, namely the nature of the task, the type of individuals who are involved in the decisions, the way the individuals’ opinions are elicited and the way the opinions are aggregated. The main drivers of NPD include: quality and speed to market; widening customer choice and expectation; competitive priorities of responsiveness, delivery, flexibility, concern for the environment and international competitiveness. For example, Wang and Lin (forthcoming) pointed out that the introduction timing of new products is impor- tant for high-technology industries to gain premium pricing and higher sales volume. A NPD project in nature should possess four latent abilities: delivering value to the customer; being ready for change; valuing human knowledge and skills; and forming virtual partnerships (McCurry & McIvor, 2002). NPD is thus a key factor for survival for business firms. Most of the fast-growing companies achieve above 50% of their total sales from the new products developed within 5 years (Lee, Lee, Koo, & Yan, 1996). Not only is the technology changing rapidly, but the process of the commercialization of technological change–the industrial innovation process–is also changing. Nowadays due to the increasingly competitive climate, more and more managers are forcing themselves to update on the range of factors that deter- mine product innovation success. Takeuchi and Nonaka (1986) indicated that all profits of new products would account for 30– 40% of total sales. Griffin (1997) represented a substantial antici- pated increase in the profit impact of new products. Sales from establishments which were part of the business five years earlier represented 32.4% of total annual sales. Especially, high-technol- ogy industries attained a great percentage of 42.3% and this in- creased continuously. Even so, the average failure rate of new products also reached a great percentage of 41%. In sum, the firms are in urgent need of developing a specialized NPD performance evaluation mechanism and platform for their effective manage- ment and for enhancing business competitiveness further. It is however difficult and laborious to measure NPD perfor- mance using traditional crisp value directly as the process of NPD performance measurement possesses many intangible or qualitative factors and items. Linguistic variable representation is therefore favorable for experts to express and evaluate the ratings of NPD under such a situation. The fundamentals of 2-tuple fuzzy linguistic approach are to apply linguistic variables to stand for the difference of degree and to carry out processes of computing with words easier and without information loss during the integra- tion procedure (Herrera-Viedma et al., 2004). That is to say, deci- sion participants or experts can use linguistic variables to estimate measure items and obtain the final evaluation result with proper linguistic variable. It is an operative method to reduce the decision time and mistakes of information translation and avoid information loss through computing with words. 3. Fuzzy linguistic computing approach Many aspects of different activities in a real world cannot be as- sessed in a quantitative form, but rather in a qualitative one, i.e., with vague or imprecise knowledge. Whereas characteristics of the fuzziness and vagueness are inherent in various decision-mak- ing problems, a proper decision-making approach should be capa- ble of dealing with vagueness or ambiguity (Yager, 1995). Fuzzy set theory is a very feasible method to handle the imprecise and uncertain information in a real world. Especially, it is more suitable for subjective judgment and qualitative assessment in the evalua- tion processes of decision making than other classical evaluation methods applying crisp values (Lin & Chen, 2004; Wang & Chuu, 2004). Basic definitions and concepts of fuzzy sets are briefly re- viewed as follows; and further, notations given below will be used throughout the paper until otherwise stated. 3.1. Fuzzy number A positive triangular fuzzy number (PTFN) eA can be denoted aseA ¼ða; b; cÞ, where a 6 b 6 c and a > 0, which are illustrated in Fig. 1. The membership function, leAðxÞ, is defined as (Zimmer- mann, 1991) leAðxÞ¼ ðx � aÞ=ðb � aÞ; a 6 x 6 b ðx � cÞ=ðb � cÞ; b 6 x 6 c 0; otherwise 8>< >: ð1Þ where x takes its values on the real line. A larger leAðxÞ means a stronger degree of belongingness for x in X. Triangular fuzzy num- 9760 W.-P. Wang / Expert Systems with Applications 36 (2009) 9759–9766 https://isiarticles.com/article/2748