The Design of a Methodology for the Justification and Implementation of Process Mining
Process mining techniques allow organizations to extract knowledge from information systems that store process-related data. Process models can be (re)constructed based on process executions, the SOLL and IST position of process executions can be compared, processes can be bench-marked and simulated, and lastly, real-time operational support is provided through detection, prediction and recommendations. The value organizations can derive from these techniques is largely contingent upon the quality of their process data.Financial specialists are increasingly relying on information technology to support them in their jobs. Process mining yields greater visibility of operations. This paves the way for improved governance and control measures. Conformance checking supports internal and external auditors, compliance specialists, controllers and other related professions through testing whether the process is executed in accordance with the business rules. Process improvement specialists (e.g. lean six sigma professionals) can simulate changes in the process, to test how these changes affect the process. Detection can be used as a signaling tool, allowing organizations to take preventive or corrective measures. Prediction can be used to predict future related process parameters (e.g. workload). Lastly, recommendations provide operational employees with advice based on historic process executions.Van der Aalst (2011b) states the first step in any process mining project is its justification. However, neither the literature review, nor backward searches, provided methods for the justification and implementation of process mining projects. Therefore, this paper develops a method for the justification and implementation of process mining in organizations. A generic process mining business case framework is developed, which organizations can use as a guideline for developing their business case. Additionally, an eight phase methodology is developed, to assist organizations from their early planning stages up until reviewing the implementation
Year of publication: |
2016
|
---|---|
Authors: | Gielstra, Erik |
Publisher: |
[2016]: [S.l.] : SSRN |
Subject: | Theorie | Theory | Bergbau | Mining | Prozessmanagement | Business process management | Data Mining | Data mining |
Saved in:
freely available
Extent: | 1 Online-Ressource (54 p) |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 24, 2016 erstellt |
Other identifiers: | 10.2139/ssrn.2761939 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012994939
Saved in favorites
Similar items by subject
-
Multi-Paradigm Process Mining : Retrieving Better Models by Combining Rules and Sequences
De Smedt, Johannes, (2014)
-
Towards improving the representational bias of process mining
Aalst, Wil van der, (2012)
-
Mining the low-level behaviour of agents in high-level business processes
Ferreira, Diogo R., (2013)
- More ...