Industry 4.0 as a Cyber-Physical System study

Industry 4.0 is based on the implementation of a cyber-physical system, which includes sensors, networks, computers, offering digital enhancement and well-coordinated activities. This would create a great pool of all the workforce generations, having diverse experience, agility, and different modes of working. Millennials would add more of machine learning and Generation X and Y would be the richest source of tacit and operational knowledge. Together, they would develop solutions for catering complex and networked production and aggressive logistic management, meeting the challenges of the Industry 4.0. However, the benefits of digitization and automation can be achieved, if the different generations of workforce collaborate, cooperate, and postulate together in all the business processes. Reverse mentoring is a pristine concept and ingenious method to empower learning and encourage cross-generational connections. This chapter would elaborate on the advantage of reverse mentoring in crafting Industry 4.0 more acrobatic and quick-moving.

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A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications

Manufacturing Letters ◽

10.1016/j.mfglet.2018.01.005 ◽

2018 ◽

Vol 15 ◽

pp. 139-142 ◽

Cited By ~ 44

Author(s):

Peter O'Donovan ◽

Colm Gallagher ◽

Ken Bruton ◽

Dominic T.J. O'Sullivan

Keyword(s):

Machine Learning ◽

Physical System ◽

Industry 4.0 ◽

Fog Computing ◽

Low Latency ◽

Cyber Physical System

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Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Sustainability ◽

10.3390/su12208629 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8629 ◽

Cited By ~ 1

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Royo ◽

Juan Carlos Sánchez ◽

Lisbeth del Carmen Ng Corrales

Keyword(s):

Supply Chain ◽

Energy Consumption ◽

Real Time ◽

Physical System ◽

Machine Tools ◽

Industry 4.0 ◽

Performance Indicator ◽

Green Supply Chain ◽

Cyber Physical System ◽

Key Performance Indicator

This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

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