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A Data-Analytic Approach to Early Identification
出版RAND Corporation, 2022
主題Business & Economics / Strategic PlanningBusiness & Economics / Production & Operations ManagementComputers / Data Science / GeneralScience / Research & Methodology
ISBN19774087539781977408754
URLhttp://books.google.com.hk/books?id=RrbpzgEACAAJ&hl=&source=gbs_api
註釋Risk is a key component in any business transaction, especially transactions worth millions of dollars or more. As the Department of the Air Force acquisition professionals are aware, there are multiple kinds of risks in developing and acquiring new systems. One recurring challenge to successful acquisition program execution is poor contractor performance. When contractors are in danger of not meeting contractual performance goals, acquisition may not be fully aware of the shortfall until, for example, a schedule deadline is missed, government testing indicates poor performance, or costs exceed expectations. The authors developed and prototyped a new way to apply data analysis on a variety of government and commercial data sources to assess the relative contractor performance risks in Air Force acquisition contracts and programs. This method produces risk indicators earlier than do current information sources and metrics by analyzing workforce, costs, financial health, influence, supply chains, past performance, and other data to assess relative risk indicators. Such automated tools can help managers focus their limited resources on potential risks buried in large, diverse data and take mitigation actions based on program relevance and impact on desired risk levels. Identified outliers are indicators for acquisition professionals to apply their acumen, understanding of program priorities, and acceptable levels of risk to determine the relevancy and magnitude of the potential risks and what actions should be taken (if any) to mitigate them.