Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/66811

TitleOn the use of simulation as a Big Data semantic validator for supply chain management
Author(s)Vieira, António Amaro Costa
Dias, Luis S.
Santos, Maribel Yasmina
Pereira, Guilherme
Oliveira, José A.
KeywordsSimulation
Big Data
Data issues
Semantic validation
Supply chain management
Industry 4.0
Issue date2020
PublisherElsevier
JournalSimulation Modelling Practice and Theory
Abstract(s)Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.
TypeArticle
URIhttp://hdl.handle.net/1822/66811
DOI10.1016/j.simpat.2019.101985
ISSN1569-190X
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1569190X19301182
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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