Abstract
To gain a competitive edge, companies must continually invest in new product development (NPD), and must decide how to strategically allocate limited resources. The most critical NPD activity is the accurate assessment of the attractiveness of new products, simultaneously considering favorable factors (project value and strategic fit) and unfavorable factors (project risks), especially in robust companies in developing countries. In the NPD development process, the attractiveness of products is often evaluated using information that is imprecise or ambiguous. Fuzzy logic is well-suited to inform NPD decision-making. Thus, a comprehensive method considering both favorable and unfavorable factors, and using a fuzzy weighted average to devise a fuzzy possible-attractiveness rating (FPAR) of an NPD project for portfolio selection, is proposed in this paper. FPAR is a measurement of information, which is able to retain the multiplicity of that information. The proposed evaluation technique was demonstrated using a Taiwanese company as an example. The results indicated that this method provided an accurate assessment of overall product attractiveness, necessary for obtaining organizational buy-in, and can effectively aid managers to conduct sensitive analyses, balance the impact of changes in strategy, and receive quick feedback on the results of such changes.
Highlights
Technological innovations and new business practices, as well as mounting market competition worldwide, have forced many firms, such as Google, Apple, Alexion Pharmaceuticals, and Tesla Motors, to accelerate new product development (NPD) to maintain long-term growth and sustainability (Hammond et al 2006; Oke et al 2007)
In an increasingly competitive, globalized marketplace, NPD is crucial for the survival of high-technology firms
The proposed method considers both favorable and unfavorable factors and uses a fuzzy weighted average to build an fuzzy possible-attractiveness rating (FPAR) for an NPD project that maintains the multiplicity of linguistic meaning and the ambiguity of factor measurements
Summary
Technological innovations and new business practices, as well as mounting market competition worldwide, have forced many firms, such as Google, Apple, Alexion Pharmaceuticals, and Tesla Motors, to accelerate new product development (NPD) to maintain long-term growth and sustainability (Hammond et al 2006; Oke et al 2007). To assist managers in new product portfolio selection, numerous decision-enhancing tools, such as mathematical programming, economic models, option pricing theory, scoring models, and analytical hierarchy approaches, have been developed. Most of these techniques have both practical and theoretical limitations (Griffin 1997; Henriksen and Traynor 1999; Hans et al 2007); these approaches are unable to take holistic views, provide limited information on financial results, and offer dubious probabilities of completion (Kornfeld and Kara 2011; Griffin 1997; Kornfeld and Kara 2011). Managers typically perceive such techniques to be too difficult to use and understand (Griffin 1997; Kornfeld and Kara 2011)
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