PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management

Abstract

Most organizations need to monitor and assess their business activities. In order to support the performance analysis of the business activities in a more systematic manner, in this research, we introduce a PRocess ANalytics System, called PRANAS. The system adopts process warehouses and process cubes to support process-oriented analysis, as well as data-oriented analysis. In this research, the process warehouse and cube were designed to assess business performances for supply chain management, specifically under the SCOR standard models. Furthermore, the process cube was constructed based on process-related dimensions such as time, case type, and event class to support process mining. Finally, we exemplify how the system can be applied to process analytics with three use cases of process discovery, data analytics, and decision point analysis. It is expected that the proposed system can be a helpful reference model when business process analyst designs process analytics systems in the process-oriented perspective, as well as in the data-oriented perspective.

Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions

“…Applying deduction graph model, data visualization, and optimization to a sports equipment manufacturer, they showcased the usefulness of the analytic infrastructure for new product development. Kim et al [103] presented a PRocess ANAlytics System (PRANAS) to effectively and efficiently evaluate the operational performance of supply chains with the use of process cubes, process warehouses, process analytics, data analytics, and hybrid analytics. Er Kara et al [104] presented a supply chain risk management framework supported by data mining tools/techniques for the identification, assessment, and mitigation of different types of supply chain risks and demonstrated the use of the framework in discovering hidden and useful information from unstructured risk data.…”

Laying the ground for future cross-organizational process mining research and application: a literature review

“…Structural mechanisms assign authority and roles within coPM (Bensaou and Venkatraman, 1996). Five kinds of them can be found in the coPM literature: methodologies for conducting coPM (Canjels et al, 2019;Helm and K€ ung, 2016;Ho et al, 2009;Maruster et al, 2003), definitions of comparison metrics (Buijs et al, 2012b) and collaboration metrics (Kim et al, 2020), mechanisms for value distribution (Rott and B€ ohm, 2022b), and a language for formalizing business process compliance (Gonz alez and Delgado, 2021).…”

“…Three types of metrics for comparing variants of the same business process are introduced by Buijs et al (2012b): process model quality metrics; for example, soundness or complexity; performance indicators, such as the average execution time per case; and comparison metricsfor instance, precision. Collaborative performance metrics are presented for four dimensions by Kim et al (2020) following the balanced scorecard methodology: finance (e.g. collaborative costs reduced), partnership (e.g.…”

Big Data and AI for Process Innovation in the Industry 4.0 Era

“…Finally, the paper "PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management" by Kim, Obregon, and Jung [9] combined process-oriented and data-oriented analyses. This was accomplished using their own developed system called PRANAS, which applies process warehouse and process cube methods to assess supply chain management application performance.…”