| Summary: | 碩士 === 逢甲大學 === 工業工程學所 === 91 === In the high-technology enterprise, the research and development (R&D) plays a key role. To prevail in the business competition, an enterprise must ensure the quality, cost and delivery of products to satisfy the marketing requirements. However, risk is actually what innovation researches differentiate from traditional industries and apparently inevitable during the R&D process. The traditional risk evaluation models frequently focused on individual event not are effective as needed for an overall risk assessment of a project development. Consequently, the purpose of this article is employing the fuzzy set theory to evaluate the risk grade and building a framework for the risk assessment of a high-tech R&D project.
This paper is beginning with describing the process and work items of an R&D project through concurrent engineering actions. The risk factors associated with the project are then concluded as a result of literature studying, SWOT analysis, and judgments of experts in connection with the project characteristics, organizations and decision making. For all kind of risk factors of work items, the fuzzy values and their grades of risk probabilities are calculated and assessed by employing the fuzzy set theory. By the analysis results and applying the 80/20 principle, the 80% of resources can then be appropriately allocated to the top 20% of work items that are identified as higher-risk items and need to be controlled from the perspective of risk management.
To further validate the accuracy of risk calculations, the sensitivity analysis is conducted. A risk management program system is designed by Visual Basic incorporating Project and Access of Microsoft software to effectively perform the project. The calculation results are achieved and compared to those from the traditional qualitative analysis to verify the accuracy of the system. Eventually, the case for air-vehicle engines development project is implemented to illustrate the feasibility of the designed system based on the fuzzy set theory and provide organizations with an assistant framework on high-tech R&D risk management.
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