Advancing manufacturing systems with big-data analytics

ǂa ǂconceptual framework

Dominik Kozjek (Author), Rok Vrabič (Author), Borut Rihtaršič (Author), Nada Lavrač (Author), Peter Butala (Author)

Abstract

With the intensive development and implementation of information and communication technologies in manufacturing, large amounts of heterogeneous data are now being generated, gathered and stored. Handling large amounts of complex data - often referred to as big data - represents a challenge as there are many new approaches, methods, techniques, and tools for data analytics that open up new possibilities for exploiting data by converting them into useful information and/ or knowledge. However, the application of advanced data analytics in manufacturing lags behind in terms of penetration and diversity in comparison with other domains such as marketing, healthcare and business, meaning that the available data often remain unexploited. This paper proposes a new conceptual framework for systematically introducing big-data analytics into manufacturing systems. To this end, the paper defines a new stepwise procedure that identifies what knowledge and skills, and which reference models, software and hardware tools, are needed for the development, implementation and operation of data-analytics solutions in manufacturing systems. The feasibility of the proposed conceptual framework is demonstrated in a case study from an engineer-to-order company and by mapping the framework to several previous data-analytics projects.

Keywords

manufacturing systems;data analytics;big data;conceptual framework;

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	658.5(045)
COBISS:	17034523
ISSN:	0951-192X
Views:	20
Downloads:	5
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	proizvodni sistemi;podatkovna analitika;velepodatki;konceptualni okvir;
Type (COBISS):	Article
Pages:	str.169-188
Volume:	ǂVol. ǂ33
Issue:	ǂno. ǂ2
Chronology:	2020
DOI:	10.1080/0951192X.2020.1718765
ID:	19478717