Analyzing Influential Factors of Business Process Key Performance Indicators

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.
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