Assessing the value of information of data‐centric activities in the chemical processing industry 4.0

Abstract

The quality of information generated in data‐driven empirical studies is of central importance in Industry 4.0. However, despite the undeniable and widely accepted importance, not sufficient attention has been devoted to its rigorous assessment and analysis. Consequently, if information quality cannot be measured, it also cannot be improved, and therefore current efforts for extracting value from big data empirical studies and data collectors are exposed to the risk of generating limited findings and insights, leading to suboptimal solutions. In this article, we describe and apply a framework for evaluating, analyzing and improving the quality of information generated in empirical studies called InfoQ, in the context of the Chemical Processing Industry (CPI). This systematic framework can be used by anyone involved in data‐driven activities, irrespectively of the context and specific goals. The application of InfoQ framework to several case studies is described in detail, to illustrate its practical relevance. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3868–3881, 2018

The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company

“…The speed and breadth of adoption of IoT solutions for production in Industry 4.0 therefore depends not only on technical factors, but also on environmental, economic and social ones. On the one hand, there is the potential for greater efficiency in the use of resources and energy (Reis and Kenett 2018). On the other hand, the challenge is to maintain the pre-eminent role of man in this new production area.…”

Multiscale and Multi-Granularity Process Analytics: A Review

“…On the other hand, data resolution is a key aspect of the Quality of Information extracted from empirical studies, as proposed in the InfoQ framework developed by Kenett and Shmueli [165,166]; see also [167]. Multi-granularity provides effective tools to increase the quality of information generated in data-driven analysis by properly handling multiresolution data structures or by optimally selecting the data granularity for a given purpose.…”

Financial modeling trends for production companies in the context of Industry 4.0

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