Setting an Industry 4.0 Research and Development Agenda for Simulation – a Literature Review

Vieira, Antônio; Dias, Luís M. S.; Santos, Maribel Yasmina; Pereira, Guilherme; Oliveira, José A.

doi:10.2507/ijsimm17(3)429

Cited by 54 publications

(38 citation statements)

References 36 publications

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“…The need to improve industrial processes is, in fact, one of the main goals of Vieira et al [5] reviewed simulation studies closely related to the concept of Industry 4.0, in order to identify the boiling research directions for simulation, which are aligned with the Industry 4.0 movement. According to the authors, such studies include the use of Big Data concepts and technologies applied to SC problems, due to the possibility of capturing the detail of processes that Big Data allows, along with the ability to consider the uncertain nature of SC systems that simulation allows.…”

Section: Simulation and Big Data In Scmmentioning

confidence: 99%

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

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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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.

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Section: Simulation and Big Data In Scmmentioning

confidence: 99%

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

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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“…Desde o início da industrialização, saltos tecnológicos levaram a mudanças de paradigma que hoje são chamadas revoluções industriais [27]. A quarta revolução industrial, ou Indústria 4.0, originada na Alemanha em 2011, engloba uma série de recursos inovadores a fim de construir o conceito de fábricas inteligentes [64]. Ela é impulsionada por quatro disrupções tecnológicas: (1) o surpreendente aumento nos volumes de dados, na capacidade computacional e na conectividade; (2) o surgimento de capacidades analíticas e de inteligência de negócios; (3) nova interação homem-máquina; e (4) a transferência de instruções digitais para o mundo físico [66].…”

Section: Referencial Teóricounclassified

“…Sua aplicação pode ir desde problemas internos de logística até complexas linhas de produção, entre outros. Como pode ser visto, a simulação é uma técnica sólida com muitos anos de aplicação e tem lidado com muitos problemas diferentes [64].…”

Section: Introductionunclassified

Simulação como Tecnologia Habilitadora da Indústria 4.0: Uma Revisão da Literatura

Randon¹,

Cecconello²

2019

SCI

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No atual contexto de desenvolvimento das organizações rumo a Indústria 4.0, processos industriais são diariamente bombardeados por uma diversidade de variáveis cada vez maior, além da necessidade constante de melhorias e evoluções tecnológicas exigidas pelo mercado. Neste cenário a simulação computacional aplicada tem papel fundamental, assim como outras ferramentas, constituem as chamadas tecnologias habilitadoras. Este estudo busca compreender como esta ferramenta habilitadora está sendo percebida e implementada. Para tanto foi realizada Revisão Sistemática da Literatura, apresentando uma visão de como os artigos científicos estão sendo publicados quanto a sua distribuição geográfica, autorias, áreas de pesquisas, entre outras características. A tecnologia da simulação como ferramenta de auto aprendizado proporciona autonomia a sistemas de manufatura inteligentes, gerando tomada de decisão dinâmica e automática. Realizando feedback ao sistema tomador de decisão, a simulação gera oportunidades de melhorias de performance e previsão de riscos, aumentando os níveis de segurança. Também, extrapola os paradigmas da simulação como ferramenta de otimização estatística e posiciona-se cada vez mais no mundo da virtualização através de imagens, realidade virtual, layout fabril, entre outras representações gráficas.

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“…In what concerns simulation, as Jahangirian et al [5] and Pires et al [6] postulated, it has been widely used to model SC processes by feeding these models with statistical distributions, however, the use of transactional data to feed such models has not been extensively explored. A fortiori and to the best of the authors' knowledge, no study has used Big Data technologies to model SC systems, despite the benefits that Big Data technologies are expected to bring to simulation of SCs, in accordance with Industry 4.0 and as discussed by several studies [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

Vieira

Dias

Santos

et al. 2019

Simulation Modelling Practice and Theory

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The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.

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Setting an Industry 4.0 Research and Development Agenda for Simulation – a Literature Review

Cited by 54 publications

References 36 publications

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

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

Simulação como Tecnologia Habilitadora da Indústria 4.0: Uma Revisão da Literatura

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

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