Intelligent Process Analytics for Automated Bottleneck Prediction and Adaptive Task Prioritization Across Small and Medium Enterprises

Abstract

Digital transformation has increased the availability of operational data, creating opportunities for organizations to move beyond reactive process management toward predictive and adaptive decision-making. However, small and medium enterprises (SMEs) frequently operate with constrained resources, fragmented workflows, fluctuating workloads, and limited analytical capacity, making timely identification and resolution of operational bottlenecks particularly challenging. Intelligent process analytics integrates process data, machine learning, and decision intelligence to transform operational records into actionable insights for improving workflow performance. This study presents a framework for automated bottleneck prediction and adaptive task prioritization across SME operations. The approach integrates historical and real-time process information, including task duration, queue length, resource utilization, dependencies, workload, delays, and completion patterns. Predictive models estimate emerging bottlenecks and their operational consequences, while an adaptive prioritization mechanism dynamically ranks tasks according to urgency, dependency constraints, resource availability, delay risk, and business importance. Continuous performance feedback enables priorities and predictions to adjust as operating conditions change. The framework supports earlier intervention, improved resource allocation, reduced process delays, enhanced throughput, and more responsive operational decision-making, providing SMEs with a scalable foundation for data-driven process optimization.