Focus on Didactic Factories: IIoT Smart Box experiment

WHAT ABOUT THE EXPERIMENT? 

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The IIoT Smart Box experiment is centered on addressing the challenges faced by small and medium-sized enterprises (SMEs) in implementing Industrial Internet of Things (IIoT) architecture and Artificial Intelligence (AI) applications. These challenges include cost sensitivity, the heterogeneous nature of machinery, data security concerns, and the need for flexible system configurations. In response to these obstacles, the experiment proposes the introduction and testing of smart tools, such as smart boxes, in collaboration with SMEs and Digital Factory Advanced Manufacturing Center (DF AMC) of the University of Twente.

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At its core, the experiment seeks to facilitate the digital transformation of SMEs by providing innovative solutions that enable them to harness the benefits of IIoT and AI technologies. By leveraging smart tools and AI algorithms, SMEs can enhance their manufacturing processes, optimize operational efficiency, and improve overall productivity. Additionally, the experiment aims to develop an IIoT architecture that is low-cost, easily expandable, and customizable to the specific needs of SMEs. 

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Through collaborative efforts with SMEs and DF AMC, the experiment endeavors to overcome common obstacles encountered in digitization initiatives, paving the way for more accessible and practical solutions for SMEs in the manufacturing sector. The ultimate goal is to empower SMEs with the tools and knowledge needed to thrive in an increasingly digitalized industrial landscape, driving innovation and competitiveness in the manufacturing industry. 

 

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WHAT IS EXPECTED?  

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The experiment aims to improve product quality, productivity, and operational efficiency in SMEs by implementing real-time monitoring and predictive maintenance through IIoT and AI applications. By gradually integrating smart tools and AI algorithms into the production process, the experiment seeks to optimize Overall Equipment Effectiveness (OEE), reduce material waste, optimize production plans, and minimize energy consumption, ultimately fostering sustainable manufacturing practices.