Managing Workflow Time Overruns: A Workload-Aware Operational Management Approach Supported by Machine Learning
Keywords:
operational management, workflow time overruns, operational control, planning accuracy, workload balancing, process governanceAbstract
Workflow time overruns are recurring operational control problems rather than mere forecasting errors. When actual completion times exceed plan, managers face schedule instability, coordination losses, approval bottlenecks, and rising service costs. Using 2,500 task-level observations, this study examines how workload-aware analytics from routine workflow data can improve operational control over time overruns. The analysis treats time overrun as the main outcome and evaluates whether variables such as task type, department, priority, approval level, employee workload, estimated duration, and cost provide useful visibility into overrun risk. The results show that routine workflow data can indicate where overrun exposure tends to accumulate, especially around estimate quality, workload conditions, approval requirements, and task heterogeneity. However, the strongest managerial value of analytics lies less in replacing judgment than in improving planning discipline, estimate calibration, workload review, and exception monitoring. The study therefore reframes workflow overrun analysis as an operational control and process-governance issue.
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