ارزیابی عملکرد توسعه محصول جدید توسط محاسبات فازی زبانشناختی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2748||2009||8 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 6, August 2009, Pages 9759–9766
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 evaluation 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 information loss effectively. Finally, its feasibility is demonstrated by the result of NPD performance evaluation for a high-technology company in Taiwan.
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 enterprises have been put forward, such as more product variety, shorter 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 technological developments continue to spur technology-based innovations 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 becoming increasingly important and challenging. New product development (NPD) is undeniably vital in determining the economic success of manufacturing companies. Firms need to create and sustain competitive advantages in order to survive in today’s highly competitive business environment. One major determinant of sustaining competitive advantage is the ability of the firms to develop and launch successful new products. Differentiation through NPD is therefore one of the most effective strategies 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 introduction 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 and Sherman et al., 2005). Gemser and Leenders (2001) conclude that being innovative with respect to design and design strategy can enhance competitiveness regardless of industry 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 requires the firm to establish a specialized evaluation mechanism and platform for NPD performance. However, the decision-making domain of NPD is highly complex and uncertain due to a demanding environment characterized by increased globalization and segmentation of markets, increased levels of product complexity, changing customer needs, and shorter product life cycles (Belecheanu, Pawar, Barson, Bredehorst, & Weber, 2003). New product introduction in today’s technology-driven 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, increased 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 appropriately, 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. Experts devote themselves to judge the NPD performance measurement by their experiential cognition and subjective perception in the decision-making process. However, there exists a considerable 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 evaluators. Consequently, developing an easy way to calculate the performance ratings while the processes of evaluation integration and to manipulate the operation of qualitative factors and expert judgment appropriately in the evaluation process of NPD could brook no delay. In this paper we propose a suitable model based on 2-tuple 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 measurement 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 method 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.
نتیجه گیری انگلیسی
Differentiation through NPD is one of the most effective strategies for achieving success. Obviously NPD is a key factor for survival for business firms in drastic conditions. With all the strategy concern, NPD evaluation model should be an applicable mechanism for companies to explore the core vantage and guide the company in the face of the challenge in the future; likewise, it would be advantageous for managers to effectively carry on and enhance current NPD mode in the light of diverse performance levels of criterion and related considerations. This paper presents a fuzzy linguistic computing approach to deal with heterogeneous information, and information loss problems that are to be averted. The proposed approach ministers to the managers in comprehending the performance of NPD. Especially it takes advantage of 2-tuple fuzzy representation of linguistic variables to express the qualitative evaluation of measured criteria of experts’ subjective opinions. Also, 2-tuple fuzzy operation method effectively assists in dealing with the aggregation of rating and weighting among criteria. Particularly the proposed method supplies companies with a flexible manner to perceive the present situation of NPD and to handle the performance evaluation decisions of NPD in a practical business environment.