A Linguistic Approach to Measuring the Attractiveness of New Products in Portfolio Selection

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). New product portfolio selection is crucial and vital for successful innovation. However, portfolio decisions are difficult because of the combinatorial complexity involved in allocating limited resources to develop a multiplicity of new products; moreover, necessary information is often incomplete or ambiguous, and selection criteria are interdependent and often conflict (Loch and Kavadiaa 2002; Zapata et al. 2008). 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. However, 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). Furthermore, mathematical portfolio approaches have tended to provide inadequate understandings of risk and information, and are unable to handle multiple interrelated criteria (Lin and Chen 2004). They generally fail to recognize interrelationships with respect to the payoffs of combined utilization of resources (Griffin 1997; Lin and Chen 2004; Ghasemzadeh and Archer 2000), and address only some of the aforementioned issues (Zapata et al. 2008). Finally, managers typically perceive such techniques to be too difficult to use and understand (Griffin 1997; Kornfeld and Kara 2011).

Portfolio management is a complex, dynamic process involving substantial decision-making. Managers must allocate a limited set of resources to projects in a manner that balances risk and reward, and aligns with their strategies, which may not be easily expressed in numerical values. Conventional “crisp” evaluation approaches are inadequate to suitably or effectively inform such decisions. People are capable of understanding and analyzing obscure and imprecise events, but this level of comprehension is difficult to incorporate into existing analytical methods. Therefore, NPD decisions are executed primarily on the basis of expert opinions expressed not in numbers, but in linguistic terms, which are inherently vague. One way to objectively capture the meaning of linguistic terms is to use the fuzzy logic approach to associate each term with a possibility distribution (Dubois and Prade 1988). By using the concepts of multicriteria decision-making (MCDM), and a fuzzy-weighted average technique, a fuzzy possible-attractiveness rating (FPAR) was devised to conduct for new product portfolio selection. The proposed fuzzy logic new product portfolio selection model (FLNPPSM), integrating strategic fit and both product value and risk into a flexible formulation, can effectively aid managers dealing with ambiguity and complexity in the portfolio selection process. The development of FPAR and FLNPPSM is the main contribution of this paper.

Portfolio management is critical, yet complex, and a wide variety of project selection models have been devised. These can be grouped into three general categories: strategic management approaches, benefit measurement methods, and mathematical programming approaches (Cooper et al. 1998).

Strategic management approaches emphasize the connection between innovative projects and strategy, and illuminate issues of risk and strategic balance in portfolios (Cooper et al. 1998). In portfolio selection, the decision to allocate resources to various new core and new generation products, or to product families, derivatives, or enhancements, is driven by business strategy. Strategic considerations dominate the decision to pursue certain R&D projects or new product development. In the literature, a number of tools, such as the bubble diagram (Roussel et al. 1991), the portfolio map (Groenveld 1997), and the “strategic bucket” method, have been developed to evaluate the fitness of NPD projects with a company’s strategy. The strategic bucket method illustrates the links between projects and a company’s strategy; however, it does not address portfolio balance or the maximization of returns.

Benefit measurement methods maximize portfolio value by financial or nonfinancial measures. In the literature, a number of approaches, such as the merit-cost value index (Henriksen and Traynor 1999), the Q-sort (Souder and Mandakovic 1986), the analytical hierarchy process (Brenner 1994), data envelopment analysis (Linton et al. 2007), financial models (Chun 1994), probabilistic financial models (Bard et al. 1988), and option pricing theory (Perlitz et al. 1999), have been developed to estimate the potential benefits of NPD projects. In these methods, projects are selected sequentially by score. Often criticized for their over-reliance on financial data, these methods are unable to optimize the mix of projects, and cannot account for uncertainty. Consequently, certain studies have used the criterion of conditional stochastic dominance (Ringuest et al. 2000) or the mean-Gini analysis (Ringuest et al. 2004) to assist risk-adverse decision-makers in conducting projects with numerous uncertain variables.

Mathematical programming models optimize the commercial value of a portfolio within predetermined resource constraints, by using linear, nonlinear, integer, dynamic, goal, and stochastic mathematical models. These models maximize value, but pay scant attention to balance, or to the alignment of a portfolio with a company’s strategy. Such models rely on financial projections of the commercial value of each project (Dickinson et al. 2001), and principally rely on a single criterion; consequently, they may suffer from a high degree of uncertainty and reduced credibility (Cooper et al. 1988). However, in recent years, mathematical programming and project selection models have become more practical and realistic. Beaujon et al. (2001), for example, developed a mixed integer programming model to determine an optimal project portfolio, considering project concepts and the estimated project value within the selected portfolio. Dickinson et al. (2001) proposed the concept of a dependency matrix, representing complex interdependencies between projects, and also developed an optimal portfolio model to be used over multiple time periods.

NPD is characterized by a high degree of complexity, ambiguity, and vagueness, in addition to the difficulties involved in accurate quantitative evaluation. Fuzzy logic is a powerful tool to inform decisions involving imprecise, ambiguous, and vague phenomena that can only be expressed in linguistic, rather than numerical, terms. Fuzzy logic therefore enables effective and efficient quantification of imprecise information in the reasoning process, and decision-making based on vague and incomplete data (Machacha and Bhattacharya 2000). In recent decades, to reduce complexity, ambiguity, and uncertainty, numerous fuzzy analysis and decision tools have been developed to assist managers in making more satisfactory NPD screening decisions (Coffin and Taylor 1996; Machacha and Bhattacharya 2000; Kuchta 2001; Lin and Chen 2004; Lin and Hsieh 2004; Chen et al. 2006; Carlsson and Fuller 2007; Wang and Hwang 2007). For example, Coffin and Taylor (1996) and Machacha and Bhattacharya (2000) applied fuzzy logic to both software products and R&D project selection. Kuchta (2001) used fuzzy numbers to present the current net value and resource utilization of individual projects. Other researchers first used the fuzzy analytic hierarchy process to prioritize the relative importance of various evaluation criteria (Chen et al. 2007). Moreover, both a general weighted average (Chen et al. 2006) and a fuzzy weighted average (Chen et al. 2007) have been applied to consolidate fuzzy ratings and weightings of evaluation criteria to obtain the fuzzy performance value of each project. Because the fuzzy weighted average approach produces a more informative result, Lin and Chen (2004) used this technique to devise a fuzzy possible-success rating for a new product. Lin and Hsieh (2004) used a system based on fuzzy logic and fuzzy integer linear programming to develop a strategic portfolio selection model. To balance risk and revenue, align projects with strategy, and estimate the value of R&D project options, Carlsson and Fuller (2007) incorporated future cash flow estimates into their portfolio selection model. Wang and Hwang (2007) considered each stage of a new product project similar to purchasing an option on a future investment, and developed a fuzzy real-options valuation model, combined with fuzzy integer linear programming, to conduct portfolio selection.

Ayag and Ozdemir (2009) developed a fuzzy analytical network process-based approach to evaluate a set of conceptual design alternatives developed in an NPD environment to determine the option that satisfied both the needs and expectations of customers, and the engineering specifications of the company. Lin et al. (2010) combined the analytical hierarchy approach and the fuzzy integral method with MCDM techniques to construct a value-created model to evaluate new mobile communication package offerings. Combining fuzzy set theory and multicriteria group decision-making, Wei and Chang (2011) used the criteria of project performance, project delivery, and project risk, to propose a new approach to select NPD project portfolios. In the same vein, Ho et al. (2013) also developed a multicategory and multistandard approach using fuzzy value-based time limits to conduct NPD project portfolio selection.

To sustain long-term consistent corporate growth in the current, turbulent, increasingly challenging environment, robust companies, especially in developing countries in Asia, must allocate a limited set of resources to projects in a manner that balances risk and reward, and aligns with their strategies. Previous studies have generally concentrated on only a few dimensions for evaluating projects, such as benefits (Carlsson and Fuller 2007; Wang and Hwang 2007), corporate strategies (Lin and Hsieh 2004; Lin et al. 2010), and benefits and risks (Lin and Chen 2004; Wei and Chang 2011), and have tended to neglect potential risks, such as organizational factors or technical uncertainties (Chen et al. 2006, 2007). Furthermore, models based on complex methodologies that are not generally understood are unconvincing and cannot obtain organizational buy-in (Griffin 1997). Hence, a comprehensive, flexible project selection method that offers an overall possible-attractiveness rating is required to meet the practical needs of many companies.

For the purpose of application, the basic properties of fuzzy logic needed in this study are introduced, and an additional discussion can be found in a book by Klir and Yuan (1995).

The FLNPPSM, as shown in Fig. 1, contains three main stages. The first stage is the new product prescreening, in which senior managers determine a set of critical characteristics which a new product must meet, informed by their business and new product strategies. The second stage is individual new product scoring: on the basis of changes in the business environment, managerial goals, and company competency, managers develop a set of criteria which a new product should meet, to rate its attractiveness. In the third stage, projects are prioritized and resources allocated. The stepwise procedure is as follows:

The proposed evaluation procedure was implemented in new product portfolio selection in an international information technology (IT) product company in Taiwan, to evaluate its effectiveness.

In an increasingly competitive, globalized marketplace, NPD is crucial for the survival of high-technology firms. Companies must evaluate their NPD from the perspective of new projects in their portfolios. This research highlighted the importance of new product portfolio selection. Because of the complexity, ambiguity, and incomplete information involved in new product portfolio selection, a comprehensive fuzzy-logic-based portfolio selection model applying linguistic approximation and fuzzy sets was developed. The proposed method considers both favorable and unfavorable factors and uses a fuzzy weighted average to build an FPAR for an NPD project that maintains the multiplicity of linguistic meaning and the ambiguity of factormeasurements.

This model was developed using the concept of MCDM and adapted for an IT product provider, which served as a case study to validate the model and approach. The company and managers involved in this case study were generally comfortable and pleased with the design and use of the approach. The perceptions of the managers employing this approach, compared with traditional approaches such as factor-weighting, were that the fuzzy attractive rating does aggregate and purify the subjective judgments and clarify the overall indication of the relevant possibility, covered by imprecision and ambiguity, and also ensures that the decisions made in the selection process are not biased. Furthermore, this approach allows managers a high degree of flexibility in decision-making. In the case study, the FPAR of \(\hbox {P}_{1}\) and \(\hbox {P}_{3}\) were (0.378, 0.622, 0.823) and (0.369, 0.624, 0.834), respectively. Qualitatively, these ratings suggest that the proposed product \(\hbox {P}_{3}\) was more attractive than \(\hbox {P}_{1}\), whereas the proposed product \(\hbox {P}_{1}\) was a more certain success than \(\hbox {P}_{3}\). Thus, the main contribution of this research is the development of an algorithm which clarifies and purifies the ambiguity, imprecision, and uncertainty of evaluation factors, appropriately modifies the fuzzy weighted average technique to incorporate both favorable and unfavorable factors, and enables software to be developed to support MCDM, especially when considering all the factors in new product portfolio selection. This study provides potential value to companies by offering a rational structure to reflect on imprecise or ambiguous phenomena commonly present in many business environments, and accounting for the uncertainty of each factor to assure a reasonably realistic and sharp evaluation. This study is potentially valuable to researchers in its demonstration of still another application of fuzzy logic. Furthermore, compared with existing studies (Lin and Hsieh 2004; Chen et al. 2006, 2007; Carlsson and Fuller 2007; Wang and Hwang 2007; Lin et al. 2010; Wei and Chang 2011), the proposed approach offers the following distinct features:Although the case study demonstrated the usefulness of the model for new product portfolio selection, there is room for future validation and improvement. Therefore, it is hoped that more managers will be encouraged to adopt this method. A single case study or a number of case studies do not necessarily demonstrate the relative performance and success of this model. Further research is required to fine-tune the proposed model and to compare the efficiency of different models for new product portfolio selections.

Evaluation levels and personnel involved inevitably vary between firms, and situations and requirements vary according to both products and companies. Firms in high-tech industries, focused on gaining the competitive advantage through innovation, may employ criteria and strategies different from those employed in firms in mature industries, seeking to compete as low-cost providers of proven technologies.

Finally, the fuzzy logic approach has certain limitations. Competitive situations and requirements vary from one company to another; hence, each company must establish a unique membership function appropriate to its specific environment and considerations. Furthermore, the computation of a fuzzy weighted average is complicated and not easily appreciated by managers. However, the necessary assessment calculations can be executed using a computer, to increase accuracy while reducing both the calculation time and error.