# Response Surface Methodology Using Observational Data: A Systematic Literature Review
## 2 RSM Overview for Response Optimization
### Rsm-Od  
The data-driven concept as a part of smart manufacturing has grown and has become a recent issue in some research, as proposed in other literature reviews [28; 29]. Moreover, the rapid development of data acquisition systems supports the application of data-driven  
Figure 2: Research fields applying RSM. analysis. In the manufacturing process, a data acquisition system, especially those with automatic sensor-based data recording, will produce massive mounts of data that potentially contain information about the characteristics of the process/equipment. This system records data on the equipment parameters and product characteristics; as examples of the data-acquiring process, as explained by [5,15], some smart sensor devices can collect data from various types of equipment as a part of the data-driven technology. Therefore, several researchers argued that data analysis should be applied to obtain useful information. Other research successfully performed analyses based on these collected data for industrial application purposes, such as product quality prediction [30], preventive equipment maintenance [16], the process optimization purposes [4], similar to our topic. For practical purposes within manufacturing or laboratory scale, with the provided dataset or data acquiring system, the RSM-OD analysis is preferred because it does not need to interrupt the ongoing production, nor does it require exceptional equipment parameter adjustments for experimenting. Other papers argued that it reduces experimental costs [2]. Both sets of authors from [3,4] considered observational data as alternatives to designing experiments and applied them for continuous semiconductor and tire production, respectively. A large number of recorded data opened up opportunities to use them as a part of the process optimization system based on the data-driven concept. Both research papers showed how the RSM concept incorporates observational or historical data as the basis for process optimization. Specific iterative procedures, such as the selection of potential factors, the identification of stages in the dataset, and the search for a subset of observations with similar characteristics to the designed experiment, were proposed to treat the dataset to become suitable to adopt RSM. In addition, some papers with laboratory-scope experimentation implemented RSM based on observational or historical data with a specific approach called historical data design (HDD), which is provided by Design Expert(r) v.11 software from Stat-Ease, Inc., 1300 Godward St NE, Suite 6400, Minneapolis, MN, USA. Although it is more similar to ordinary multiple regression analysis fitted to observational data, HDD is a type of observational data-based DoE within the RSM analysis. For details on performing HDD, refer to the software manual guide from [31] based on a case study by [32]. Both [6,8] explicitly mentioned and applied HDD, where previous, un-designed, and experimental or observational data were used as inputs for RSM analysis to optimize energy consumption and plastic strength. Another similar paper gave a different perspective; the authors of [33] worked on an additive manufacturing process to predict surface roughness, and real-time data-driven modeling techniques were applied to minimize the prediction error. A real-time approach requires no assumptions for the data and does not need to evaluate the significance of the factors; its main target is to obtain the minimum error in the predicted response with no model interpretation required [34]. Meanwhile, the standard RSM proposed by [1] applies the philosophy of three stages in its analysis (Figure 1), with several required assumptions in the data, such as factor independence, treatment randomization, and factor significance, to give a strong interpretation; the final target of this RSM is to obtain the optimum response by finding the optimal factor setting/level. The next section explains that this approach treats the dataset's variables, features, and responses as input and output.