## 2 Literature Review
### Proposed Problem Statement and Research
[PERSON] and [PERSON] [71] listed the examples of common RPA applications as data entry or information retrieval, automatic e-mail response, user registration, identity verification, and processing of customer orders and payment. Data digitization can aid in automating process steps in Industry 4.0. [PERSON] and [PERSON] [37] listed the steps required to automate an industrial process and referred to it as a \"pre automation\" sequence, which includes:  
Step 1--Stabilization. Step 2--Standardization. Step 3--Optimization. Step 4--Automation. However, the \"pre-automation\" sequence method cannot assist in identifying areas where improvement is required, and it does not describe the tools used in each step in detail. It is important to note that no detailed step-by-step guidelines were found in literature for both identifying and transitioning manual operations to full automation using digitized data. Other existing techniques can be utilized in combination with the \"pre-automation\" sequence to add more structure to the framework. By coupling industry 4.0 capabilities with Lean and Six Sigma methodologies, further improvements could potentially result in standardizing and fully automating the production process because the purpose of Lean and Six Sigma is to reduce waste or non-value added steps, which in turn will reduce manual interactions and make the process more automated. Lean and Six Sigma principles have been used in businesses and academia for many years. However, the traditional Six Sigma method alone may be restrictive or inapplicable for some problem scenarios. For example, traditional Six Sigma alone is unsuitable in an Industry 4.0 setting because of the noisy in-process data produced from complex technologies [72]. Digitizationof data produces large quantities of unorganized data, which can be difficult and time consuming to analyze manually. The Lean and Six Sigma implementations listed in Table 1 in Section 2.1 were specific to particular issues in their respective industries with no automation intention [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. The Strategic Lean Six Sigma Framework proposed to reduce build time in an aerospace industry that required validation from new areas within the industry [25]. The authors in [27] used Six Sigma and measure-validate-predict (MVP) to predict faults using alert notifications. However, the methodology used was unclear as no detailed description was provided. The interactions between all of the variables affecting the process makes it difficult to use the traditional Six Sigma method alone; it is important to understand the process to identify problems and offer suggestions for improvements [73]. In contrast to digitized data, the traditional Lean Six Sigma data contain clean data of the required variables that can be measured manually. Digitized data require pre-processing prior to analyses because such large datasets can contain data from multiple variables, and only relevant variables are selected for analysis [74]. Therefore, the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework was utilized in literature for data mining [28; 75]. Numerous studies have shown that modifying the phases of Six Sigma methodologies or integrating data mining techniques with the traditional Six Sigma methodology can customize the process improvement method to cater to a specific problem and provide better outcome for the study [21; 25; 27; 28]. While Lean Six Sigma can help in providing tools to be used for identifying and analyzing problem areas [76] prior to the \"pre-automation\" sequence, data mining techniques can guide in handling digitized data for process optimization. Combining the techniques can allow the researcher to customize the phases of the techniques, and the use of the process improvement tools based on what the study requires. For example, one study reported that the number of defects were only reduced in the software industry with the help of Six Sigma alone, but it failed to effectively eliminate them [21].