Industry 4.0 is a concept aimed at achieving the integration of physical parts of the manufacturing process (i.e., complex machinery, various devices, and sensors) and cyber parts (i.e., advanced software) via networks and driven by Industry 4.0 technology categories used for prediction, control, maintenance, and integration of manufacturing processes. Industry 4.0, which is expected to have a great impact on manufacturing systems in the future, is attracting attention in both industry and academia. Although academic research on Industry 4.0 is growing exponentially, evidence of Industry 4.0 implementation in practice is still scarce. Moreover, the challenges industry faces when implementing the Industry 4.0 concept seem to be even less addressed. At the start of the present survey, a preliminary literature review identified a lack of comprehensive analysis of the Industry 4.0 implementation challenges. Thus, the purpose of the present article is to provide an overview of the reported Industry 4.0 implementation challenges in the relevant literature by conducting a systematic literature review. Specifically, while the present study differentiates between managerial and technological Industry 4.0 implementation challenges, the focus of the present article is on the managerial Industry 4.0 implementation challenges. This overview is performed by deriving an inductively coded Industry 4.0 technology framework that classifies Industry 4.0 technologies into ten categories: cyber physical systems, Internet of Things, big data analytics, cloud computing, fog and edge computing, augmented and virtual reality, robotics, cyber security, semantic web technologies, and additive manufacturing. The present article identifies, codes, and defines the managerial Industry 4.0 implementation challenges and derives opportunities for overcoming them.

No abstract

With the technological development of advanced technologies and the use of the Internet of Things (IoT), the number of connected devices is increasing in manufacturing processes. As devices become more and more incorporated using more processing power, the big data is generated. However, increasing the generation of big data leads to problems related to processing and analysis. The current tendency of solving the problems of processing and analysis is via Cloud Computing technologies. However, more attention is dedicated of performing computations as close to the device as possible, relying on Edge Computing technologies. Motivated by these facts, this paper provides a comparative analysis of the roles of edge computing and cloud computing, summarizing challenges and opportunities of these technologies and providing their application in Industry 4.0.

No abstract

In the last decade, researchers have focused on digital technologies within Industry 4.0. However, it seems the Industry 4.0 hype did not fulfil industry expectations due to many implementation challenges. Today, Industry 5.0 proposes a human-centric approach to implement digital sustainable technologies for smart quality improvement. One important aspect of digital sustainability is reducing the energy consumption of digital technologies. This can be achieved through a variety of means, such as optimizing energy efficiency, and data centres power consumption. Complementing and extending features of Industry 4.0, this research develops a conceptual model to promote Industry 5.0. The aim of the model is to optimize data without losing significant information contained in big data. The model is empowered by edge computing, as the Industry 5.0 enabler, which provides timely, meaningful insights into the system, and the achievement of real-time decision-making. In this way, we aim to optimize data storage and create conditions for further power and processing resource rationalization. Additionally, the proposed model contributes to Industry 5.0 from a social aspect by considering the knowledge, not only of experienced engineers, but also of workers who work on machines. Finally, the industrial application was done through a proof-of-concept using manufacturing data from the process industry, where the amount of data was reduced by 99.73% without losing significant information contained in big data.