A Data-Driven Assessment of Redesign Initiatives in Financial Management Processes
Business Process Redesign (BPR) is a fundamental approach to enhancing efficiency, compliance, and digital transformation in public sector operations. Despite extensive theoretical advancements, its application in real-world settings remains limited. This study addresses this gap by applying the BPR Assessment Framework to business processes within the Greek Public Financial Management (PFM) domain, specifically analyzing workflows from the Greek Customs Service and the Financial and Economic Crime Unit (S.D.O.E.). This research employs a structured methodology that integrates internal process metrics with clustering techniques to systematically classify processes based on their redesign potential. The findings reveal that a significant proportion of public sector workflows demonstrate high redesign capacity, highlighting opportunities for efficiency gains and improved regulatory compliance. Furthermore, this study identifies key challenges, such as organizational resistance and technological constraints, that impact BPR implementation. By demonstrating the framework’s applicability in a complex, operational environment, this study provides actionable insights for policymakers and practitioners. Specifically, the results show how structured process evaluation enables targeted redesign initiatives that streamline administrative workflows, enhance compliance with financial regulations, and support digital transformation in public administration. These insights contribute to advancing BPR practices by bridging the gap between theoretical development and real-world application, offering a replicable methodology for improving public sector efficiency.
Authors
- George Tsakalidis (ORCID: https://orcid.org/0000-0002-0889-7946)
- Kostas Vergidis (ORCID: https://orcid.org/0000-0002-2755-499X)
Institutions
- University of Macedonia (GR)
Publication Details
- Journal
- Information
- Published
- 2025-02-26
- DOI
- https://doi.org/10.3390/info16030179
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28