## 3 Results
### Digitization and Manufacturing Process Representation  
The first of the challenges identified through our \"divide and conquer\" methodology, defined in Section 2, is related to data extraction, digitization, and process representation. Hence, to address this challenge, different tasks were performed, achieving the following results. Firstly, after the KPI identification--in other words, obtaining a list of areas and possibilities in data extraction tasks--a deep analysis was conducted. Specifically, this analysis focused on comparing and contrasting different manufacturing process areas to determine the feasibility of data capture. To this end, the frequency, accuracy of the data, visualization, capture and storage, access to the data, and their traceability in the different areas of the investment casting process were analyzed. The final results of this analysis are illustrated in Figure 6.  
As shown in Figure 6, the lowest scores were observed in the \"access to data\" and \"visualization\" categories. The primary reason is that all related data are stored in complex tables, systems, or other sources that are not easily connected. Moreover, there is a lack of visualization tools to effectively distribute this information. Conversely, \"frequency\" and \"accuracy\" scored higher due to the rigorous testing procedures already established within the investment casting process for each area. This figure also guided the research team in selecting the areas to be included in this work regarding the measurements. In this way, primary and secondary coating, as well as the melting area and final inspections, were selected. Secondly, taking into account the vast number of variables that could be extracted in each area, a new analysis was conducted to identify the final list of variables to prioritize. This analysis focused on the expected impact and digitization potential of each selected variable. A summary of these results is shown in Figure 7.  
Figure 7 is a dispersion plot where the degree of simplicity in digitization is represented on the y-axis, and the impact of different variables is shown on the x-axis. Furthermore,  
each selected variable is categorized into stages, with each stage represented by a different color, as described in the legend. The legend refers to stages 1, 2, and 3, which can more accurately be translated as primary coating for stage 1, secondary coating for stage 2, and melting for stage 3. After conducting this analysis and creating the plot, the variables to be used were selected in the following order. First, the variables that appear in the top-right corner were selected. These variables, from the three areas, are labeled as priority 1 variables and must be gathered in the first iteration of the data gathering work. Second, the variables that appear in the top-left corner, labeled as priority 2 variables, were selected for the second iteration of the process. Third, the variables that appear in the bottom-right corner were selected. Finally, the remaining variables were considered. Hence, this plot was useful for determining the strategy and work plan for data gathering and process representation. Figure 6: Start plot generated as a summary of results when the different areas were analyzed and evaluated using a Likert scale for 6 different categories such as data traceability, frequency, and precision, among others. Figure 7: Dispersion plot with the selected area (i.e., primary coating—stage 1, secondary coating—stage 2, and melting—stage 3) correlating the impact, the digitization potential, and the priority of each variable to be added to the system. Then, once the variables for each area and their priority were defined, the data extraction process was executed. As a result of this work, several visualization screens were created and distributed throughout the plant. These screens were designed in close collaboration with engineers and foundry workers to ensure that the information was displayed in the most effective way and in the correct locations.