## 2 Literature Review
### Tacit Knowledge and Digitization  
Industry 4.0 is the fourth industrial revolution in manufacturing, where machines are integrated with each other, and are capable of monitoring processes and making smart decisions using training data autonomously [39; 40]. Thus, standardizing the processes by reducing human error variabilities. One key feature of Industry 4.0 is decentralized production, where all machines are connected to the cloud, allowing data to be accessed from a secured line for fault analysis and data-driven decision-making [41]. However, collecting and storing data are challenges due to non-interoperability of machines and tools [42; 43]. Deriving and sharing useful knowledge from the dataset for optimizing processes still poses as a challenge for industries as production data are usually inconsistent [44]. Implementing advanced functions, such as fault diagnosis, fast recovery, and fast reconfiguration of the machine parameters to adapt to production changes, increases the complexity of automatic machines, requiring technical understanding. The rise in complexity will require highly-educated and well-informed workers [45]. Proprietary communication protocols also need to be maintained. Data formats need to be unified to allow data to be shared across different levels of manufacturing without information being lost [46]. [PERSON] [47] explained that there are several issues that hinder standardization, which is crucial for fully transitioning to Industry 4.0. For instance, it is difficult to create a common language for all interconnected platforms for data exchange. The existing standards developed for Industry 4.0 may not be applicable to all systems; new or modified standards are required in such cases. Data exchange between platforms require secured lines to protect the privacy of businesses. More importantly, there is a lack of data availability for effective decision-making in the manufacturing sector, because acquiring data related to machine faults for monitoring the degradation progress is very challenging, and impossible in certain scenarios [48]. One study explained that the absence of detailed and functional computer numerical control (CNC) databases compels operators to select suitable machining parameters based on tacit knowledge [46]. Tacit knowledge is defined as the valuable heuristic knowledge accumulated by the operator over years of experience [49]. However, studies have shown that sharing tacit knowledge is difficult due to the knowledge gap and the terminologies used between workers from different roles and experience levels. For instance, process engineers might face difficulty in fully understanding a data analystand vice versa. Decision-making becomes mentally exhaustive to a skilled, knowledgeable worker, because tacit knowledge is not properly defined [50]. The machining parameters are adjusted using tacit knowledge and tested during the first-off inspections (which are wasteful, in terms of time and resources) [46]. First-off inspections are conducted when machine configuration is changed between production batches, to check whether the first part that is machined is correct. It is then assumed that the rest of the parts in the batch will be machined correctly. If the correct specification on the part is not achieved, then the machine configuration is adjusted. This is to prevent a batch of faulty parts from being produced, which can have a significant impact on revenue (i.e., reducing scrap reduces material loss and energy consumption) [51]. Processes in industries are still controlled and constantly supervised by people, leading to inefficiencies; thus, indicating that industries are far from making processes completely autonomous [52; 53]. If such issues with machine interoperability, data availability, non-automated production lines, etc., could be improved, then lights-out manufacturing is possible without human supervision, where machines can manufacture products without lights and operators [54].