## 7 Discussion and Conclusions
### Practical Aspects to Decision Support Development in Industry  
The decision support tool development contained several steps from data collection and model development to model analytics and visualization based on the user's needs. The lessons learned are generalizable to a wider audience regardless of the field. The following aspects were recognized while employing and using manufacturing data for quality improvement and decision support in industry. Figure 19: Recommended actions for an operator when a product has a bad predicted quality with Model_HYBRID. Figure 20: Recommended actions for an operator when a product has a bad predicted quality with Model_RAP. **Data collection. ** If possible, the data collection should be designed for decision support purposes. Data should contain a comprehensive set of products and production conditions with relevant parameters. In the presented application, the available data contained only a fraction of the actual repertory. In order to improve the generalizability of the model, an investment into collecting a larger data set could be made. This is advisable, especially if the model proves to be useful in product and production planning. **Surrogate model. ** It is possible to use model predictions as surrogates for actual measurement device. At Outokumpu, the absence of a surface roughness measurement device on site prevented the prediction of this quality property earlier. This article demonstrates that the surface roughness of the steel strip can be predicted with good accuracy, and the risk of a failure can be presented to the user already during the product planning phase, which enables the user to adjust the process parameters and minimize the risk of roughness. Even though the measurement campaign has ended, it is still possible to adjust the process based on the predicted quality and to improve the surface quality. Naturally, the model's performance in real-time use cannot now be verified. **Learning the process. ** With prediction models, it is possible to obtain an insight into production and better understand the relationships between process parameters and the predicted property. In this case, the analysis of the roughness prediction models uncovered the parameters of the RAP-line that have the highest impact on the surface roughness. Additionally, the hypothesis for the high importance of the hot rolling process parameters on roughness formation was disproved. The hybrid model and RAP model outperformed the HOT model. Actually, the parameters in hot rolling reflected especially the mechanical properties of the product and inherently the probable surface quality. The difference in roughness appeared mainly because of the varying RAP line parameters when comparing products with similar mechanical properties. By studying the visualizations of the model's performance, the user may learn more about the process and how the parameter settings affect the quality and how they relate to each other. **Creating new knowledge. ** It is important to understand the reasoning behind the predictions. This way, the user can verify the applicability of the results and evaluate the performance for individual observations. The interpretability of the models was improved with XAI methods (PDP, ALE, SHAP, parallel coordinates and its modifications) that highlight the significance of the process parameters in roughness formation individually for each observed product. The average performance of the model can be quite different for individual products as the chemical composition and mechanical properties determine how each product type responds to the process settings during the manufacturing. Thus, it is important to find out the rootncauses behind a high predicted risk of failure. **Role of domain knowledge.