## 5 The Technologies
### Big Data Analytics  
The paradigm shift of digital transformation towards Industry 4 implies that all processes are data-driven [4]. The volume of data collected from manufacturing processes can be huge, which is the reason why the term \"big data\" is used [2]. The intention of employing big data analytics is to interpret the massive raw data into meaningful information, which in turn enables information transparency in line with the Industry 4 design principles. The interpreted data can be used to help participants make data-driven decisions. In a smart manufacturing context, the use of AI can provide new contributions to decision-making processes by analysing and interpreting massive amounts of data collected from the whole product lifecycle [63]. Advanced AI algorithms (e.g., machine learning, neural networks) can facilitate the analysis of large volumes of data to identify the trends, patterns, and correlations at high speed [150]. Thus, AI-powered data analysis methods are broadly used and are an important contributor and differentiator in the development of Industry 4 facilities. There is a broad range of applications that are related to big data analytics in academic Industry 4 facilities. In the reviewed articles, there are seven main data-driven applications that have been discussed. The application of production process optimisation received the most contributions in the reviewed papers. [PERSON] et al. [112] applied machine learning algorithms to monitor the working status of machine tools, which helps with optimising production processes. [PERSON] [47] discussed the application of AI-empowered data analytics to reduce waste in production processes. [PERSON] et al. [101] proposed a method for optimising the performance of cooling systems by continuously monitoring the temperature and humidity of the workpieces. [PERSON] et al. [58] demonstrated the implementation of [PERSON]'s algorithm to optimise production processes. [PERSON], [PERSON], and [PERSON] [99] introduced the application of production monitoring by analysing real-time production data in their 5G learning factory. [PERSON] et al. [30] proposed a strategy to enhance the system's adaptability by monitoring the real-time system performance against the pre-set KPIs for each assembly station. [PERSON] et al. [54] proposed a method for enhancing production process automation by analysing ultrasonic testing data to position jigs. [PERSON] et al. [107] employed genetic algorithms to optimise intralogistics performance. Similarly, [PERSON] et al. [63] introduced an application using an AI algorithm to enhance the performance of drone navigation in the intralogistics system. [PERSON] and [PERSON] [2] discussed the application of big data analytics in the developed CPPS of an IFA learning factory for enhancing the performance of production planning and control. Quality management is also a fundamental discipline that can significantly benefit from employing AI-powered big data analytics. [PERSON] et al. [110] proposed a methodology to monitor in real time the assembly quality by analysing collected data on the torque, angle, and speed of the screwdriver from an assembly station. [PERSON] et al. [37] and [PERSON] and [PERSON] [55] discussed applications of online quality control by employing a machine vision system. The developed TensorFlow model training algorithm can effectively distinguish defective products. From the energy management perspective, [PERSON], [PERSON], and [PERSON] [151] proposed a method to monitor energy consumption in real time in an MR context. Linear regression and neural network algorithms were used to predict and optimise the strategy of energy management. Other than the manufacturing industry, an innovative energy management method was discussed in a learning factory for the building industry [152]. The intention was achieved by collecting and analysing the real-time data of carbon dioxide levels in the HVAC systems of buildings.