Open Access Open Access  Restricted Access Subscription or Fee Access

Analyzing Influential Factors of Business Process Key Performance Indicators


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v18i2.24410

Abstract


Monitoring and diagnosing performance issues of business processes are critical to ensure the satisfactory performance of enterprise business processes. To evaluate the quality of operations, the organizations define their own Key Performance Indicators (KPIs) for each business process. These KPIs might be related to duration, cost, risk, customer satisfaction or any other criteria. Practically, discovering influential factors of KPIs results is valuable for organizations' management. It helps in providing better understanding of the analyzed process. As a result, it supports management in taking enhancing actions to ensure better performance. In this work, we propose a new methodology that effectively discovers causal relationships between KPIs results and other process information by using the association rules algorithm and applying three steps of rules pruning processing. This methodology was compared with two other methodologies; one of them uses the decision trees algorithm and the other one uses the association rules with no rules pruning. The evaluations on real production environment data related to the procure-to-pay process show that the proposed approach can address the limitations of the other two approaches.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Association Rules; Business Process; Decision Tree; Key Performance Indicator; Process Analyzing; Rules Pruning

Full Text:

PDF


References


Gröger, C., Schwarz, H., Mitschang, B., Prescriptive Analytics for Recommendation-Based Business Process Optimization, Lecture Notes in Business Information Processing (Page: 25 Year of Publication: 2014).

Wetzstein, B., Leitner, P., Rosenberg, F., Brandic, I., Dustdar, S., Leymann, F., Monitoring and Analyzing Influential Factors of Business Process Performance, IEEE International Enterprise Distributed Object Computing Conference, (Page: 141 Year of Publication: 2009 ISBN: 978-0-7695-3785-6).

Ordonez, C., Comparing Association Rules and Decision Trees for Disease Prediction, Proceedings of the international workshop on Healthcare information and knowledge management, (Page: 17 Year of Publication: 2006 ISBN: 1-59593-528-2).

Gröger , C., Niedermann , F., Mitschang , B., Data Mining Driven Manufacturing Process Optimization, Proceedings of the World Congress on Engineering (Page: 4 Year of Publication: 2012 ISBN: 978-988-19252-2-0).

Kang, B., Lee, S. K., Min, Y. B., Kang , S. H., Cho , N. W., Real-time Process Quality Control for Business Activity Monitoring, International Conference on Computational Science and Its Applications IEEE (Page: 237 Year of Publication: 2009 ISBN: 978-0-7695-3701-6).

Dasgupta, G. B., Shrinivasan, Y. B., Nayak, T. K., Nallacherry, J, Optimal Strategy for Proactive Service Delivery Management Using Inter-KPI Influence Relationships, Lecture notes in computer science (Page: 131 Year of Publication: 2013).

B. Wetzstein, A. Zengin, R. Kazhamiakin, A. Marconi, M. Pistore, D. Karastoyanova, F. Leymann, Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation, (2012) Journal of Systems Integration, 3 (1), pp. 3-12.

M. Castellans, F. Casati, U. Daya, M. Shan, A Comprehensive and Automated Approach to Intelligent Business Processes Execution Analysis, (2004) Distributed and Parallel Databases, 16 (3), pp. 239-273.

H. Y. Abu Mansour, Rule pruning and prediction methods for associative classification approach in data mining, Doctoral Thesis, University of Huddersfield, Huddersfield, UK, 2012.

J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques (Elsevier Inc., 2011).

P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining (Pearson, 2005).

Shrinivasan, Y. B., Dasgupta, G. B., Desai, N., Nallacherry J., A Method for Assessing Influence Relationships among KPIs of Service Systems, Lecture notes in computer science, (Page: 191 Year of Publication: 2012).

Li, W., Han, J., Pei, J., CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules, Proceedings of the 2001 IEEE International Conference on Data Mining (Page: 369 Year of Publication: 2001 ISBN: 0-7695-1119-8).


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize