# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 5 Conclusions and Future Work  
The typical techniques of data-oriented analytics, such as regression, classification, and clustering, do not consider process-related aspects such as the order of business activities and the timestamp of generated data. Although a few process-oriented analytics such as process mining techniques  
Figure 7: Two examples of combined data-oriented and process-oriented analytics. (**a**) Discovered model with decision points using ProM; (**b**) Decision tree classifier for a decision point split by company size.  
consider various case data as well as process-related data, the concrete methods have not yet been clearly developed. Besides, the existing techniques of process mining often focus on only a single well-defined process rather than a process data complicatedly combined with the data storage. However, business processes change over time due to various reasons, such as work structure change and economic situation changes. Clearly, when business analysts analyze operational business performance, they should consider the execution of business process along with the design of business data. For this reason, the concepts of process cube and warehouse were presented in this paper to illustrate how they can be used for both of data-oriented analytics and process-oriented analytics.  
In this paper, we designed the process analytics system called PRANAS that contains a process warehouse and a process cube. The analytics system was implemented for the operational performance analytics in supply chain management under the SCOR standard model. As the performance measures for operational process analysis, the collaborative BSC (cBSC) was designed by extending four perspectives of BSC in terms of collaboration among business partners. To illustrate the process analytics tools, three types of examples of analytics were given: Process-oriented analytics, data-oriented analytics, and hybrid analytics. In particular, the operation performance on a multidimensional level using the designed process cube was analyzed.  
Previous studies on integration between data analytics and process analytics were not sufficient. To supplement the research necessity, the concept of process warehouse and cube can be useful. The proposed framework based on the process warehouse and cub is expected to be a helpful guide for designing business performance systems to be implemented using recently advanced techniques of data mining, as well as process mining. This is because the system was designed to support both aspects of data-oriented analytics and process-oriented analytics.  
Although we showed a system implementation supporting the proposed process analytics system in this paper, many challenges still remain. The main purpose of operational analytics is to provide \"near real-time\" analytics in organizations, and therefore an automated procedure for generating insightful reports could be proposed. With this, the decision-making process inside the company would be systematically improved in terms of business process intelligence.  
Conceptualization, A.K. and J.-Y.J.; methodology, A.K. and J.-Y.J.; software, A.K. and J.O.; validation, A.K. and J.-Y.J.; investigation, A.K., J.O. and J.-Y.J.; Writing--Original draft preparation, A.K. and J.O.; Writing--Review and editing, J.O. and J.-Y.J.; visualization, A.K. and J.O.; supervision, J.-Y.J.; project administration, J.-Y.J.; funding acquisition, J.-Y.J. All authors have read and agreed to the published version of the manuscript  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1064125).  
The authors declare no conflict of interest.