Intelligent Process Mining for Predictive Bottleneck Detection and Performance Optimization in SAP Cloud Business Workflows

Abstract

The increasing adoption of SAP Cloud solutions has transformed enterprise operations by enabling integrated, scalable, and data-driven business processes. However, organizations continue to experience operational inefficiencies caused by process delays, resource constraints, redundant activities, and unexpected deviations from established workflows. Predictive performance analytics, supported by process mining and bottleneck detection, provides an effective approach to identifying these challenges and forecasting future process outcomes. This study examines how process mining techniques can be applied to SAP Cloud business processes to improve operational visibility, predict performance deviations, and identify potential bottlenecks before they significantly affect business outcomes. The proposed approach integrates event log extraction, process discovery, conformance checking, predictive modeling, and performance analysis to evaluate process execution patterns. Historical process data are analyzed to identify recurring delays, determine critical activities, and estimate case completion times. Machine learning techniques, including regression, classification, and decision tree models, are considered for predicting process delays and identifying cases at risk of exceeding predefined performance targets. The methodology also incorporates key performance indicators, such as cycle time, throughput, waiting time, and rework frequency. The study establishes a conceptual framework for integrating predictive analytics into SAP Cloud environments to support proactive decision-making, continuous process improvement, and resource optimization. The anticipated contribution is a systematic, data-driven approach to improving process transparency, operational efficiency, and business process performance in cloud-based enterprise systems.