Leveraging Transactional Business Data to predict Employee Workload Satisfaction in Operations: An Empirical Study – Part 1

We examine whether transactional Enterprise Resource Planning (ERP) data can predict employee-perceived workload satisfaction in a metal-processing facility. Drawing on 127 working days of daily employee surveys across four logistics gates, we link each day's responses to operational records from four SAP tables: (COOIS) production confirmations, (LT060) external transport orders, (LT061) internal transport orders, and (MB51) material movements. Two gates register mean stress scores five to six times higher than a third. Internal transport activity correlates negatively with perceived problems at two gates, suggesting a buffering effect; at others, material volume rather than transport frequency seems to drive stress. Mid-week workload is consistently elevated. Our results confirm that ERP metrics can explain a bounded but meaningful portion of subjective workload variance, and that workplace-/ gate-level modeling substantially outperforms generalized approaches. These findings offer actionable insights for operations managers seeking to proactively monitor workplace social sustainability through data already available in existing information systems.