دانلود مقاله ISI انگلیسی شماره 21986
عنوان فارسی مقاله

مدیریت عدم قطعیت به بهبود تصمیم گیری در مدیریت پرتفولیو NPD با یک سیستم تخصصی فازی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21986 2012 18 صفحه PDF سفارش دهید 8400 کلمه
خرید مقاله
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عنوان انگلیسی
Managing uncertainty to improve decision-making in NPD portfolio management with a fuzzy expert system
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 39, Issue 10, August 2012, Pages 9868–9885

کلمات کلیدی
- ( ( مدیریت نمونه کارها - توسعه محصول جدید () - انتخاب پروژه - عدم قطعیت - سیستم تخصصی فازی
پیش نمایش مقاله
پیش نمایش مقاله مدیریت عدم قطعیت به بهبود تصمیم گیری در مدیریت پرتفولیو NPD با یک سیستم تخصصی فازی

چکیده انگلیسی

The importance of new product development (NPD) for a company’s growth and prosperity is emphasized and a number of methods have been suggested to help decision-making for NPD project portfolio management. In spite of their utilities, however, little attention was paid to develop a supporting system for portfolio management that can help quick but careful decision-makings under uncertainties. Therefore, this research proposes a decision-making framework that uses a fuzzy expert system in portfolio management for dealing with the uncertainty of the fuzzy front-end of product development. For the purpose of developing the framework, we adopted the three tools – strategic bucket for strategic resource allocation, scoring models for evaluating projects and portfolio matrixes for balancing projects – to find an optimal set of projects in the portfolio. In particular, this research established fuzzy inference-based models for evaluation criteria which are too ambiguous to be numerically described. Also, based on the evaluation results, the final selection of projects is made by an expert system, which can encompass the operational knowledge and company strategy in the rule-based system. The suggested framework was applied to the portfolio analysis in an electronics firm in Korea and verified its feasibility.

مقدمه انگلیسی

As a management strategy, new product development (NPD) is critical for the survival and growth of companies in a rapidly changing market. Successful NPD can provide increased sales, profits, and competitive advantage for most companies: many leading high-tech companies, for example, have found that more than 50% of their current sales come from new products (Balbontin, Yazdani, Cooper, & Souder, 2000). Therefore it is apparent that a firm’s NPD strategy is a primary determinant of performance. Particularly these days, consumer markets are becoming more versatile and technology is changing more rapidly than ever. Thus, new products need to be adapted to variable and dynamically changing markets. These uncertainties have forced a firm to dedicate much effort to NPD, being attentive to the needs of customers and the vicissitudes of modern technology for maintaining a market share. However, although companies are continuously striving to develop new products using much resource, NPD pressure is exacerbating the risk factor and causing abnormally high failure rates in the early stages of development. NPD success depends on the ability to predict potential demands in the market and to select the most feasible NPD candidates for the demands. We therefore need an effective decision-making process: a process that can accurately evaluate numerous NPD projects with limited resources and make a sound selection of the optimum set of products. Further, the necessity of such a systematic and judicious decision-making process is highlighted particularly for NPD projects, which are often hard to stop once initiated. NPD strategies can be realized by implementing an objective decision-making process through successful portfolio management, which includes the development of product and technology roadmaps that link business strategy and technology planning. Through portfolio management, companies can make various NPD decisions in association with both long-term and short-term strategies; they can also make decisions on strategic investment and resource allocation to achieve business goals. Building a strategic NPD portfolio while giving due consideration to business goals and constraints is an important and challenging task. As a result, various methods including multi-criteria decision-making tools (e.g. analytic hierarchy process (AHP)) and optimization techniques (e.g. linear programming), have been proposed to help evaluate the characteristics of NPD projects and build optimal portfolios. In spite of the meaningful contributions of these methods to portfolio management, most of the existing methods fail to reflect the uncertainty of portfolio decision-making. Moreover, the potential use of these methods as practical management models is limited because they apply the same evaluation criteria to all projects during the decision-making process, even though various selection criteria can be chosen to match the project characteristics. To overcome this limitation, we propose a decision-making method that uses a fuzzy expert system in portfolio management for dealing with the uncertainty of the fuzzy front end of product development. Depending on the market environment, marketing evaluations in the planning phase of a new product are often conducted in an uncertain or ambiguous state, particularly in cases involving the expected sales and profits. This paper establishes fuzzy inference-based portfolio evaluation models for items which are too ambiguous to be numerically evaluated. With this evaluation model, major NPD projects that should be planned under uncertainty in a firm can be evaluated and prioritized. For this purpose, we developed a portfolio expert system which facilitates the selection of right projects to develop balanced investment R&D programs and satisfy the goal of portfolio management. The research results are expected to support effective and efficient decision-making in association with NPD strategy, especially in environments where markets and technology are changing rapidly.

نتیجه گیری انگلیسی

When new markets are appearing, the product life cycle is getting shorter and the costs of innovation are getting higher, companies have to keep introduce new products to the market and strategic new product planning is essential to success. Accordingly, most companies analyze their project portfolio each year for efficient resource allocations and alignment the projects with corporate strategies. Prudent analysis is required because portfolio decision-making significantly affects not only annual sales and profits but also long-term growth of the company. To help make NPD decision-makings, this research suggests an expert system for portfolio analysis based on the work by Cooper (1994). The suggested system uses fuzzy inferencing to analyze the matrices of the scoring model and manages portfolios to ensure that the projects are well balanced in terms of periods, risks and profits. The system framework is flexible and extendable. It easily applies the portfolio evaluation rules to comply with the corporate strategies and directions in changing markets and can be accessed on Web applications. In spite of these meaningful contributions, this research is subject to a few limitations and further research is needed. First, the suggested expert system-based approach focuses more on evaluation process than evaluation criteria and decision-making rules. The system needs to be improved by elaborating a scoring method and decision-making rules. Second, we use the same if-then rule regardless of the project types. However, because of the variety of project types in a strategic bucket, we need to apply different selection criteria for each type of project. Third, we focus on two common types of portfolios. In the future, we need to design a portfolio that reflects more versatile standards for balancing all the aspects of a project. Last, further research is needed on the prioritizing of projects, particularly with regard to old and new models based on the research results of our portfolio expert system.

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