2196. Designing the Information Entropy Performance Indicator (IEPI): A DSRM-Based Approach to Managing Process Uncertainty in Business Operations
Invited abstract in session WD-34: Applications of Knowledge Work Technology, stream Advancements of OR-analytics in statistics, machine learning and data science.
Wednesday, 14:30-16:00Room: Michael Sadler LG10
Authors (first author is the speaker)
| 1. | Apostolos Mouzakitis
|
| PhD Studies, New York College Greece | |
| 2. | Anastasios Liapakis
|
| Department of Archival, Library & Information Studies (ALIS), University of West Attica |
Abstract
Business Process Management (BPM) frameworks optimize workflows and manage complexity, using Key Performance Indicators (KPIs). While effective in stable environments, traditional KPIs fail to capture uncertainty and variability, especially in regulated domains like accounting. This highlights the need for alternative ways to quantify and manage uncertainty. This study presents the Information Entropy Performance Indicator (IEPI), an entropy-based measure of uncertainty in BPMN 2.0. IEPI is based on Managerial Infophysics, linking BPM with entropy-driven decisions. Grounded in information entropy, IEPI quantifies process variability and provides actionable insights to enhance operational predictability. The study employs the Design Science Research Methodology (DSRM) to iteratively design, implement, and refine IEPI. Entropy-based metrics improve process evaluation and resource allocation. The study focuses on developing and implementing IEPI in BPM. Future validation is planned; however, this phase explores feasibility. Findings connect entropy principles with BPM optimization, extending practical use. While initially BPM-focused, this lays the groundwork for entropy-driven metrics in finance, and supply chains.
Keywords
- Management Information Systems
- Continuous Optimization
- Efficiency Analysis
Status: accepted
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