## 2 Literature Review
### Proposed Problem Statement and Research  
Automation is a key aspect of Industry 4.0, which is still very challenging for some industries to adopt [61]. This is because full automation across the factory floor requires interconnected machines, reliable communication lines, data collection from multiple sensors, standardized processes, expert knowledge, and skills to tackle the increased complexity, etc. Standardization can address the interoperability issues in machines and data [43; 62]. There was a direct correlation observed between the degree of standardization and the RPA usage in a process in organizations [63]. [PERSON] et al. [64] presents an overview of how process mining and RPA has benefitted industries where RPA automates the tasks that are highly repetitive and error free, since processes are required to be simplified and standardized before RPA can be applied. One study [65] presented an overview of the implementation of RPA and described that the function of the RPA needs to be specified, as software robots are data-related, integration-related, or process-related. The data-related function allows data transfer, formatting, and analysis, while the integration-related function ensures connection and control between different machines and processes. The process-related function facilitates event triggers and control. The key RPA skills are listed as, collect and collate information; validate; record data; calculate and generate results; plan; transport and connect; and report. Depending on the level of human interaction to maintain with the process, RPA applications can be of three types--attended (performs alongside human), unattended (performs without human interaction), and hybrid (can perform with or without human) [66]. Researchers are investigating tools and approaches to identify automation opportunities, as the field is still nascent. This is explained by the low volume of application and adoption of RPA [67]. The adopted RPA have limited and specific functionalities, such as customer service and risk assessment [33]. To address this issue, the authors in [68] proposed a methodology to identify the level of automation (manual, hybrid or automated) of an existing task from textual description via supervised machine learning. The limitation of this study was the reliability of the proposed RPA approach. Further testing and real-world implementation were suggested to validate the approach. [PERSON] and [PERSON] [69] reviewed the motivation and challenges in adopting RPA and found that, despite the benefits gained from successful RPA implementation, most companies are not adopting RPA because the compatibility and integration of RPA tools for the IT sector are still not understood clearly. Another study [32] presented the basic concept and challenges involved in a pipeline robotic process mining (RPM) tool. It was explained in this study that there is a risk of identifying partially automated tasks for full automation, in certain cases, because the robotic software is incapable of determining the partially automated task with the given input data. The information or data to identify potential tasks to automate requires cleaning to improve the accuracy of the process discovery algorithm, as explained by the study conducted by [70] on emerging trends in RPA. The European Commission [43] stated that the data currently available in industries for re-use are limited. This indicates that data are processed manually because collecting, processing, and storing data are still challenging. Rule-based, knowledge-based, and learning-based software robots use predefined rules, information search, and machine learning techniques, respectively, to analyze given data in RPA [63]. Rule-based software robots are mostly used compared to the other two. As the complexity of the process increases, RPA adoption decreases. Therefore, RPA adoption for industrial manufacturing processes is not common due to the complex nature of the processes [63].