Details

Autor(en) / Beteiligte

• Vieira, António AC
• Dias, Luís MS
• Santos, Maribel Y
• Pereira, Guilherme AB
• Oliveira, José A

Titel

On the use of simulation as a Big Data semantic validator for supply chain management

Ist Teil von

• Simulation modelling practice and theory, 2020-01, Vol.98, p.101985, Article 101985

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Simulation should be used to evaluate the semantics of data in Big Data contexts.•Proposal of a classification of the data issues that simulation helped to identify.•Data of organizations requires considerable improvements to correct semantic issues.•Semantic data issues have considerable impact on simulations in Big Data contexts.•Even with Big Data, certain processes may need to be modelled without using dataAbstract. 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.