Suchergebnisse

For an R& ; D institution to design a specific high investment cost product, the budget is usually &lsquo ; large but limited&rsquo ; . To allocate such budget on the directions with key potential benefits (e.g., core technologies) requires, at first and at least, a priority over the involved design criteria, as to discover the relevant decision knowledge for a suitable budgeting plan. Such a problem becomes crucial when the designed product is relevant to the security and military sustainability of a nation, e.g., a next generation fighter. This study presents a science education framework that helps to obtain such knowledge and close the opinion gaps. It involves several main tutorial phases to construct and confirm the set of design criteria, to establish a decision hierarchy, to assess the preferential structures of the decision makers (DMs) (individually or on a group basis), and to perform some decision analyses that are designed to identify the homogeneity and heterogeneity of the opinions in the decision group. The entire framework has been applied in a training course hold in a large R& ; D institution, while after learning the staff successfully applied these knowledge discovery processes (for planning the budget for the fighter design works and for closing the opinion gaps present). With the staffs&rsquo ; practical exercises, several empirical findings except for the budgeting priority (e.g., the discrimination between &lsquo ; more important criteria&rsquo ; against the less important ones) are also interesting. For some examples (but not limited to these), it is found that the results from using two measures (statistical correlation vs. geometrical cosine similarity) to identify the opinion gaps are almost identical. It is found that DMs&rsquo ; considerations under various constructs are sometimes consistent, but often hard to be consistent. It is also found that the two methods (degree of divergence (DoD) vs. number of observed subgroups (NSgs)) that are used to understand the opinions&rsquo ; diversity under the constructs are different. The proposed education framework meets the recent trend of data-driven decision-making, and the teaching materials are also some updates to science education.

A recent study proposed a systematic &ldquo ; (budgeting) knowledge discovery educational framework&rdquo ; (BKDEF). This BKDEF is focused on guiding staff training courses for enhancing the ability to allocate the &ldquo ; large but limited&rdquo ; budget for single, high-cost product design. However, except for its initial application to support the budget planning for the next generation fighter design, the framework&rsquo ; s effectiveness is still awaiting further scrutiny. This study fills the gap by providing the &ldquo ; second application&rdquo ; of BKDEF, which is to support another similar decision for designing the medium-altitude long-endurance unmanned aerial system (MALE UAS). This paper verified the effectiveness of the framework through an empirical application and obtained the knowledge required to allocate a budget for MALE UAS design following the group-opinion basis. In addition, the original analytical style for the last &ldquo ; decision analysis&rdquo ; phase of BKDEF, which included pure quantitative analytical items in order to understand the similarities and diversities in the individual opinions, was replaced by a comparative study to discover the homogeneity and heterogeneity between the two budgeting decisions in a larger scope. As a consequence, the two criteria sets did not overlap despite both decisions being related to military aircraft design. The absolute weights for the MALE UAS design criteria were more balanced than those for the air-superior fighter design, even if the size of the criteria set was larger. The results pave a way for future studies on how other military aircrafts are designed, as more confidence about the use of a BKDEF can be gained from increasing applications, thus more insightful aerospace knowledge can be exploited in comparisons with these works.