A Systematic Framework for Assessing the Quality of Information in Data-Driven Applications for the Industry 4.0. Issue 18 (2018)

• Record Type: Journal Article
• Authors: Reis, Marco S.
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 18(2018)
• Page Start: 43
• Page End: 48
• Publication Date: 2018
• Subjects: Quality of information -- InfoQ -- Industry 4.0 -- Big Data -- Predictive analytics; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.244
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Managing and improving the quality of information generated in data-driven empirical studies is of central importance for Industry 4.0. A fundamental and necessary condition for conducting these activities is to be able to measure the quality of information - " If you can not measure it, you can not improve it " (Lord Kelvin). It is somewhat surprising that, with so many efforts devoted to take the most out of the available data resources, not much attention has been paid to this key aspect. Therefore, in this article we described and apply a framework, the InfoQ framework, for evaluating, analyzing and improving the quality of information generated in the variety of data-driven activities found in the Chemical Processing Industry (CPI). This systematic framework can be used by anyone involved in conducting these activities, irrespectively of the context and the specific goals to achieve. For instance, it can either be used to provide a preliminary assessment of the project risk, by analyzing the adequacy of the data set and analysis methods to achieve the intended goal, as well as to perform a SWOT analysis on an ongoing project, to improve it and increase the quality of information generated, i.e., increasing its InfoQ. The framework is applied to a real world case study in order to illustrate its implementation, utility and relevance. The author recommend its routine adoption, as part of the Definition stage in any data-driven task, such as in Lean Six Sigma projects, exploratory studies, on-line and off-line process monitoring, predictive modelling and diagnostic & troubleshooting activities.