Process Analytics Approach for R&D Project Selection

摘要: RD however, they are not effective, as fail to consider the real insight of core tasks. Other decision models that analyze tasks effective but inefficient when handling large amounts submissions, and suffer from irrelevant assignment. Furthermore, largely ignore deep back-end data such quality reviewers (e.g., citation impact their produced research) effect social relationships in project selection processes essential for identifying interdisciplinary proposal evaluation. In light these deficiencies, this research proposes a novel hybrid process analytics approach decompose complex reviewer assignment into manageable subprocesses applies data-driven cum systematically triangular perspective via framework achieve high operational efficiencies high-quality It also analyzes big scientific databases generates visualized decision-ready information support making. The proposed has been implemented aid largest funding agency China tested. test results show potential add great benefits, including cost saving, improved effectiveness, increased business value.