文章基本信息

标题：A General Framework for Data Uncertainty and Quality Classification
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作者：Vanessa Simard ; Mikael Rönnqvist ; Luc Lebel 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：13
页码：277-282
DOI：10.1016/j.ifacol.2019.11.181
语种：English
出版社：Elsevier
摘要：It is often assumed that data used to plan operations and supply chain activities is accurate. But in the presence of uncertainty, this assumption is known not to be entirely true. In this context, it becomes relevant to evaluate if a planning decision is appropriate in light of partially accurate data. This paper proposes a general framework for data analysis in order to provide a quality evaluation of the information used in the decision-making process. To this end we propose a process to quantify data quality by comparing “measured” data to “real” data. We use a hybrid approach combining multiple data quality assessment techniques as well as different alternative sources of historic data. A classification phase then rates and «tags» data for proper consideration for decision-making. Such classification provides insights into the level of uncertainty associated with the data. This paper demonstrates the approach developed using a case study from the forest sector. The approach can be adapted to other industrial sectors.
关键词：KeywordsData qualityUncertaintyDecision-making processForest industry