## 6 Discussion of Results  
The combined LSS, CRISP-DM methodology, and the \"pre-automation\" sequence was successfully executed, and the process was improved in terms of reduced manual steps, reduced cycle times, and improved product output by using newly digitized CNC-CMM data. The process cycle time was reduced by 3.76% and the output was 4.48% higher after implementing first-off removal, which was the removal of the non-value added manual step since the capability analysis proved that the process was stable with the capability index Cpk > 1.67. These values obtained help in answering the research question by confirming that digitized CNC and CMM data can be used to identify, reduce, or remove manual non-value added interactions from a medical device manufacturing process using the \"pre-automation\" sequence, LSS, and CRISP-DM methodology to automate and improve the process. A sample size of 330 parts were machined and measured for analysis. However, the data collected lacked in both quantity and quality. This made the capability analysis very challenging. Although the defect rates were low and the process was stable, the lower quantity of scrap that resulted from the CNC tool and machine set-up can be prevented with better training and by completely automating milling operation 2 via an expert system, which will help standardize the process by reducing operator interaction. Removing the first-off inspection process further reduces manual interaction in the overall process. It was vital to inform and work with the operators from the initial phase of this study because some operators feel more productive with more manual interactions, and they would not have welcomed the idea of reduced labor. Furthermore, reduced manual actions will allow the operators to focus on other tasks. The reduction of cycle time and increase in product output showed that the methodology was effective at identifying the manual step that did not add value to the process, and could be removed; thus, allowing standardization and automation. RPA described in literature for automation can automate processes by automating repetitive actions within the processes while our method can automate by reducing manual steps, thus improving the overall automation of the process. The novel methodology selection flowchart aided in selecting the appropriate strategy to approach the non-standard automation issue on the factory floor. The \"digitization of data\" addition to a traditional Six Sigma process improvement technique facilitated the capability study significantly, as the analysis would not have been as efficient with manually acquired data. The transformation of digitized data from tacit knowledge allowed a continuous automatic flow of data that was not prone to human errors. Traditional Six Sigma approaches are only limited to small-scale applications when data are involved. The data mining techniques in CRISP-DM and the process improvement tools in LSS greatly helped in analyzing a large dataset and preparing the data for capability study for making the process better. The \"pre-automation\" sequence added emphasis on the automation objective of this study, which was achieved by standardization via the removal of a manual step in the process. The flow of the steps fit better with the combined methodology as it expanded the pool of tools to use and tasks to carry out in each step. The combined steps bridged the gaps in the methodology and assisted in meeting the main objective of the study. The methodology proposed is repeatable and can be deployed to optimize any process regardless of availability of data. This is because automatic flow of data can be obtained using tacit knowledge to create digitized data in absence of sensors and historical data. Analysis of digitized data will add value to the process by successfully identifying areas that require standardization and automation. Digitization of data enabled process automation, as it is a crucial element in analysis for automation. Raw production data are noisy, which is not ideal for automation.