Industry 4.0: Opportunities for Enhancing Energy Efficiency in Smart Factories

From the very knowledge of Industry 4.0, its implementation is carried out in all segments of society, but we still do not fully understand the breadth and speed of its implementation. We are currently witnessing major changes in all industries, so new business methods are emerging. There is a transformation of production systems, a new form of consumption, delivery, and transportation, all thanks to the implementation of new technological discoveries that cover robotics and automation, the internet of things (IoT), 3D printers, smart sensors, radio frequency identification (RFID), etc. Robotic technology is one of the most important technologies in Industry 4.0, so that the robot application in the automation of production processes with the support of information technology brings us to smart automation (i.e., smart factories). The changes are so deep that, from the perspective of human history, there has never been a time of greater promise or potential danger.

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APPLYING INDUSTRY 4.0 AND EDUCATION 4.0 TO ENGINEERING EDUCATION

Proceedings of the Canadian Engineering Education Association (CEEA) ◽

10.24908/pceea.vi0.13859 ◽

2019 ◽

Author(s):

Irina Neaga

Keyword(s):

Higher Education ◽

United Kingdom ◽

Engineering Education ◽

Learning Analytics ◽

Industry 4.0 ◽

Research Work ◽

Undergraduate Level ◽

Industrial Companies ◽

Master Level ◽

Smart Factories

This research work-in-progress deals with a holistic analysis of the impacts of Industry 4.0 (I4.0) for engineering education especially for University undergraduate (level 4-6), master (level 7) and PhD related manufacturing, automotive engineering and supply chain management programmes in United Kingdom higher education institutions. This analysis aims at providing support for further consolidated recommendations to enable the development of higher education engineering curriculum for enhancing I4.0 application for smart organisations and industrial companies within the digital supply chains. Also the paper provides an analysis of advancement from digitalisation in engineering education to the implementation of Education 4.0 and related practices of smart labs, and simulation of smart factories leading at the learning factory. A conceptual framework to support the application of big data and learning analytics in the School of Engineering from University of Wales Trinity St David, Swansea, United Kingdom has been identified, discussed and intended to apply in the context of applying learning analytics.

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Comparing Approaches for Evaluating Digital Interventions on the Shop Floor

Technologies ◽

10.3390/technologies6040116 ◽

2018 ◽

Vol 6 (4) ◽

pp. 116 ◽

Cited By ~ 1

Author(s):

Francisco Lacueva-Pérez ◽

Lea Hannola ◽

Jan Nierhoff ◽

Stelios Damalas ◽

Soumyajit Chatterjee ◽

...

Keyword(s):

Industry 4.0 ◽

Shop Floor ◽

Use Case ◽

Individual Level ◽

Ict System ◽

The One ◽

Comparison Of The Results ◽

Future Work ◽

Self Learning ◽

Smart Factories

The introduction of innovative digital tools for supporting manufacturing processes has far-reaching effects at an organizational and individual level due to the development of Industry 4.0. The FACTS4WORKERS project funded by H2020, i.e., Worker-Centric Workplaces in Smart Factories, aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To achieve this, understanding how to develop such tools is as important as assessing if advantages can be derived from the ICT system created. This study introduces the technology of a workplace solution linked to the industrial challenge of self-learning manufacturing workplaces. Subsequently, a two-step approach to evaluate the presented system is discussed, consisting of the one used in FACTS4WORKERS and the one used in the “Heuristics for Industry 4.0” project. Both approaches and the use case are introduced as a base for presenting the comparison of the results collected in this paper. The comparison of the results for the presented use case is extended with the results for the rest of the FACTS4WORKERS use cases and with future work in the framework.

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