Site selection is one of the basic vital decisions in the start-up process, expansion or relocation of businesses of all kinds. Construction of a new industrial system is a major long-term investment, and in this sense determining the location is critical point on the road to success or failure of industrial system. One of the main objectives in industrial site selection is finding the most appropriate site with desired conditions defined by the selection criteria. Most of the data used by managers and decision makers in industrial site selection are geographical which means that industrial site selection process is spatial decision problem. Such studies are becoming more and more common, due to the availability of the Geographic Information Systems (GIS) with user-friendly interfaces. Geographic information systems (GIS) are powerful tool for spatial analysis which provides functionality to capture, store, query, analyze, display and output geographic information. Geographic Information Systems are used in conjunction with other systems and methods such as systems for decision making (DSS) and the method for multi-criteria decision making (MCDM). Synergistic effect is generated by combining these tools contribute to the efficiency and quality of spatial analysis for industrial site selection. This paper presents a successful solution for spatial decision support in the case of spatial analysis of Vojvodina as a region of interest for industrial site selection

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.

During the last decade, we have witnessed steady movement of industry and academia toward Industry 4.0. Industry 4.0 is a concept aimed at achieving the integration of physical and cybernetic parts of the manufacturing process via networks and driven by Industry 4.0 technology categories used for the prediction, control, maintenance, and integration of manufacturing processes (i.e., cyberphysical systems, Internet of Things, big data analytics, cloud computing, fog and edge computing, augmented and virtual reality, robotics, cybersecurity, semantic web technologies, and additive manufacturing). The research on implementing Industry 4.0 is lagging behind the degree of attention Industry 4.0 attracts. Moreover, contrary to the exponential growth that Industry 4.0 research shows, the implementation of Industry 4.0 in practice is less often covered in the relevant literature. The challenges of implementing Industry 4.0 are even less frequently addressed. Industry 4.0 implementation challenges can be either managerial or technological. In our recent paper, we analyzed managerial implementation challenges. This article, as a continuation of our previous work, identifies, codes, and defines the technological Industry 4.0 implementation challenges. It further analyzes hindering relationships between Industry 4.0 technologies and identifies main challenge themes important for system designers. Finally, this article derives opportunities in the form of recommendations for overcoming current and future technological implementation challenges.

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.