# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 1 Introduction  
With the rapid development of technology, the size and availability of data are constantly increasing. Companies need to analyze their data intelligently to acquire competitive advantages in terms of efficiency and responsiveness [1]. Business analytics is a decision support system that enables managers and analysts to take effective and timely decisions in business activities [2]. Meanwhile, business process analytics is a specialized system that helps to improve business process execution by analyzing operational data recorded in event logs that contain activity events, which have been recorded in process-aware information systems such as enterprise resource planning (ERP) and supply chain management (SCM) systems [3]. Although business analytics offers both types of data-oriented and process-oriented analysis tools, few studies have investigated how to integrate the two approaches in a concrete and effective manner [4; 5; 6]. Business analytics using regression, classification, and clustering usually do not consider the order of data generation because time information is rarely contained in a general form of data, e.g., tabular data [7]. However, the context information related to business process often crucially affects the target business values of interest such as delivery time and service quality. Therefore, process analytics can provide the missing link between the data and the process aspects. Recently, although many process mining methods have been developed to analyze process execution data, most of thetraditional process mining techniques, such as process discovery and conformance checking, mainly focus on the structure of business processes, not the process-related data. Hence, process analytics tools need to integrate the two sides of analytics (i.e., data and process) in a balanced way to enable effective and efficient process data analysis. To support the integrated analysis of data and process, a concept of process cubes was proposed by [PERSON] [8]. The process cube is inspired in the online analytical processing (OLAP) and extends the concept into process repositories. A process cube is created from an event log and its related database, in which different dimensions such as event class, case type, and time are defined. Like an OLAP cube, a process cube allows the analyst to drill-down or roll-up data and zoom into slices of the data, as well as reorder the dimension. Furthermore, just as an OLAP cube is used for analyzing and reporting operational performance, a process cube can also be used in a similar way to evaluate operational performance. In this paper, we introduce a PRocess ANAlytics System, named PRANAS, which was developed to effectively and efficiently evaluate operational performance of supply chain operations using process cubes and process warehouses. The system provides an environment for both data-oriented analytics and process-oriented analytics. The system can store in the process warehouse the business performance data that are generated in supply chain execution, and the process cube can provide the aggregated process data through convenient OLAP functions such as slicing, dicing, rolling-up, and drilling-down. The process data extracted from the system can also be used as the inputs of data mining, as well as process mining, for the purpose of advanced analytics. There are insufficient studies on the design of process analytics systems that integrate two analytics aspects: Data analytics and process analytics. This research presents a good guidance for balancing the benefits of the two analytics tools using process warehouses and cubes. The process warehouse and cube can provide proper filtered data fractions that will be used for the recently advanced analytics of both data analytics and process analytics for performance management in terms of business process execution.