Abstract
Enterprise workflows are affected by workload fluctuations, resource shortages, task dependencies, approval bottlenecks, processing delays, and changing service level requirements. Conventional workflow systems often identify performance problems after delays have occurred, limiting proactive intervention. This study proposes an Explainable Predictive Analytics Framework for Enterprise Workflow Risk, Delay Detection, and Adaptive Resource Allocation. The framework integrates process event data, task duration, queue length, workload, resource availability, dependencies, historical bottlenecks, and service level requirements to predict delay probability and remaining processing time. An uncertainty layer assesses prediction reliability, while explainability and counterfactual analysis identify influential risk factors and examine feasible interventions. A resource-aware decision layer considers intervention effects, costs, and finite resource capacity when selecting adaptive actions. The framework is designed for evaluation with public process-mining and synthetic enterprise datasets against rule-based and conventional predictive approaches. Key measures include delay prediction accuracy, delay reduction, cycle time, throughput, resource utilization, and service level compliance.