## 2 Literature Review
### Proposed Problem Statement and Research
Another study reported that the data-mining technique CRISP-DM lacks a detailed method description in literature and existing quality management tools can be utilized for process improvement. It was also noted in that study that data mining was more capable at handling a large dataset and analyzing the unknown parameters than Six Sigma [75]. According to [PERSON], [PERSON], and [PERSON] [77], integrating Lean thinking with Industry 4.0 presents a far better framework to reduce waste and improve processes than Lean methodology alone. As explained earlier in Section 2.1, Lean can be combined with RPA for automating tasks in a process since the automation opportunities discovery using RPA is very limited and the Lean strategy has helped in identifying the action to be automated. However, RPA can only be applied to repetitive tasks that run 24/7 in the background [29]. Table 2 summarizes the advantages and the gap in each of the methodologies described in this paper. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Advantages** & **Gaps** \\ \hline Lean & \begin{tabular}{c} Focuses on process flow and \\ reduces waste \\ \end{tabular} & \begin{tabular}{c} Lacks statistical analysis and \\ Industry 4.0 data capabilities \\ \end{tabular} \\ Six Sigma & \begin{tabular}{c} Focuses on process capability and \\ reduces variation \\ \end{tabular} & \begin{tabular}{c} Lacks Industry 4.0 data capabilities \\ \end{tabular} \\ Lean Six Sigma & \begin{tabular}{c} Reduces waste and variation \\ Posseses Industry 4.0 data capabilities \\ \end{tabular} & \begin{tabular}{c} Lacks Industry 4.0 data capabilities \\ \end{tabular} \\ CRISP-DM & \begin{tabular}{c} Posseses Industry 4.0 data capabilities \\ \end{tabular} & \begin{tabular}{c} Lacks a detailed method description \\ \end{tabular} \\ RPA & \begin{tabular}{c} Allows processes or tasks to \\ be automated \\ \end{tabular} & \begin{tabular}{c} Lacks the capability to automate complex \\ processes. The problem identification step is not \\ well understood or validated \\ \end{tabular} \\ \begin{tabular}{c} \"Pre-automation\" sequence \\ \end{tabular} & \
\begin{tabular}{c} Lists the general guidelines to achieve \\ process automation \\ \end{tabular} &
\begin{tabular}{c} Lacks the problem identification step and \\ detailed description of tools used. Moreover, no \\ information is available on Industry 4.0 \\ data capabilities \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methodology comparison. The authors of this paper created a novel methodology to assist decision-making in selecting the optimal combination of methodologies to use for process improvement in a data-rich environment. This is shown in Figure 1. Priority was given to the goals and data availability to select the appropriate methodologies. Figure 1: Process improvement methodology selection flowchart. Since the aim of this study is to automate the medical device manufacturing process used as a case study by analyzing digitized data, the LSS, CRISP-DM, and the \"pre-automation\" sequence methodologies were selected from the flowchart in Figure 1. The manufacturing process was standardized by using digitized data that was previously manually or not collected at all and automated by removing the problems associated with non-standardized complex decision-making and operations, and human error in data collection and analysis. The combined method proposed adds Industry 4.0 and digitization capabilities to LSS, while adding structure to the existing CRISP-DM methodology via LSS. The \"pre-automation\" sequence also adds an automation framework to LSS and CRISP-DM, while LSS and CRISP-DM adds a defined structure to process digitized data for analysis in the \"pre-automation\" sequence. The proposed methodology also addresses the gaps in RPA implementation, as described in the literature, in terms of automation opportunity identification, by providing an alternative method to identify processes and automate them.