Abstract
Enterprise Resource Planning (ERP) systems are widely used to manage business documents along a business processes and allow very detailed recording of event data of past process executions and involved documents. This recorded event data is the basis for auditing and detecting unusual flows.
Process mining techniques can analyze event data of processes stored in linear event logs to discover a process model that reveals unusual executions. Existing techniques assume a linear event log that use a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model.
We present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. We identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. The technique was validated in two case studies and reliably revealed unusual flows later confirmed by domain experts.
Process mining techniques can analyze event data of processes stored in linear event logs to discover a process model that reveals unusual executions. Existing techniques assume a linear event log that use a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model.
We present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. We identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. The technique was validated in two case studies and reliably revealed unusual flows later confirmed by domain experts.
| Original language | English |
|---|---|
| Title of host publication | Enterprise Modeling and Information Systems Architectures : Proceedings of the 7th International Workshop on Enterprise Modeling and Information Systems Architectures, EMISA 2016: Fachgruppentreffen der GI-Fachgruppe Entwicklungsmethoden für Informationssysteme und deren Anwendung, Vienna, Austria, October 3-4, 2016 |
| Editors | J. Mendling, S. Rinderle-Ma |
| Place of Publication | Aachen |
| Publisher | RWTH Aachen |
| Pages | 5-8 |
| Number of pages | 4 |
| Publication status | Published - 3 Oct 2016 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Volume | 1701 |
| ISSN (Print) | 1613-0073 |
Bibliographical note
This article summarizes problem, approach, and selected findings of a study published as Xixi Lu, MarijnNagelkerke, Dennis van de Wiel, and Dirk Fahland. Discovering Interacting Artifacts from ERP Systems. Services
Computing, IEEE Transactions on, 8(6), 2015 doi:10.1109/TSC.2015.2474358 [Lu15].