# Response Surface Methodology Using Observational Data: A Systematic Literature Review
## 5 Synthesis and Discussion  
The use of observational data in RSM is not without its critics. This practice contradicts the golden standard in RSM and runs considerable risks of being used as an alternative to experimental data. The authors of [38] wrote that using observational data as a replacement for experimental data is risky because of the absence of controllable factors, spurious correlation, and the rise of potential multicollinearity or nonorthogonality. This finding was similar to the problem of semiconductor production in the work of [3] and the slurry thickening process in [39], where observational data contained undetected and uninterpreable multicollinearity, given the application of typical observational-based regression analysis and the need for careful handling ([40]). This opinion was also strengthened by the authors of [19], who wrote a systematic literature review of classic RSM development and showed that orthogonality between factors should be reached to perform individual analyses of each factor. Moreover, the ideal experimentally-based RSM accommodates the procedure of the steepest ascent for the shifting of factor levels in a specific direction toward a stationary optimum response point [1,19]. The use of observational data presents a challenge in conducting this procedure, given the limited range of factor levels. The optimization region is also limited to these available level ranges, as observed in all of the RSM-OD references. The literature review questions in the previous section served as a guide for the writing order, starting with descriptive and bibliometric analyses. Later, the synthesis stage was performed in line with answering LRQ1 to LRQ3 and continued with the discussion  
Figure 8: Distribution of the rationales based on data types. section. Some treemaps used in this paper simplified the interpretation of the descriptive and research questions answered. Treemaps are used to hierarchically graph structured information, which uses 100% of the available graph space [41], and acted as an excellent application for the supporting systematic literature review in [20]. LRQ1: What are the rationales for using observational data as an alternative to conducting a real RSM experiment? Approximately 70.51% of the papers employed observational data as the input for RSM, 23.08% were based on previous experimental data, and the remaining 6.41% referred to real experiment data (Figure 8). Observation-based data were obtained depending on the kind of data-acquiring system in the process being studied, and previous-experiment-based data were collected from associated research. The data contained the factor (or X variables) and response (Y variables) with continuous scales, as required by the RSM analysis. The rationale with the highest percentage in Table 4 is potential information that may exist within the observational data. The next highest percentage is the flexible factor level (or design space), where an RSM analysis should be flexible enough to accommodate uncontrolled factor levels within observational data. Moreover, the difficulties in fully controlling the factor levels during a continuous process showed the limitations in conducting designed experiments and provided data that were a better option. This rationale also revealed a high percentage. Several papers acquired real experiment data but used RSM-OD because of difficulties in controlling the factor levels. They assumed the real experimental data as being observational and argued that modifying the RSM approach based on a nondesigned experiment was easier than conducting a formal standard RSM. LRQ2: What condition within historical data can be adapted to RSM? Conducting the DoE experiment ensures the orthogonality between the factors, and the ANOVA can separate each variance for the independent interpretation of their effects ([42]).