BDA-enabler Architecture Based on Cloud Manufacturing: the Case of Chemical Industry

Document Type : GOL20

Authors

1 TIC Lab International University of Rabat Sale AlJadida, Morocco

2 CERADE, Esaip école d'ingénieur, Angers, France

3 IERT, Cadi Ayyad University, Marrakech, Morocco

4 Department of computer science, Algiers I University, Algeria

5 ST2I, ENSIAS Rabat, Mohammed V University, Rabat, Morroco

Abstract

With the advent of cloud manufacturing (CM), alongside the maturity of development approaches and systems in the manufacturing industry, has led to the integration of these initiatives into Industry 4.0 to achieve higher performance. In fact, the implementation of Industry 4.0 is a real opportunity for the process industry which is only at the very beginning of its deployment. However, the integration of cloud manufacturing requires the fully digitalization of industrial systems and the implementation of big data management process. Indeed, the lack of resources to handle the huge flows of data in transit and the lack of standards and interoperability is the biggest challenge to the large-scale adoption of smart manufacturing. To get around this problem, it is necessary to put in place management and analysis solutions for big data to facilitate data acquisition, process monitoring, anomaly detection and predictive and proactive maintenance. In addition, the implementation of a smart manufacturing architecture based on big data analytics (BDA) requires a lot of resources in terms of storage and computing power, which is not always available in an industrial context. Thus, it has become essential to offer suitable manufacturing models for the implementation of big data analysis services that meet the new requirements of the manufacturing sector. In this paper, a case study in one of the main African Phosphates Company will be presented. Thus, we will propose a BDA-enabler architecture based on Cloud manufacturing to identified digital opportunities and key benefits regarding performance management, production control and maintenance.

Keywords

References

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International Journal of Supply and Operations Management
Volume 9, Issue 3
August 2022
Pages 251-263

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