Amine Bourhaba

I designed and operated a detailed workforce management model for a large-scale support operation, covering hourly demand forecasting, AHT-based capacity planning, staffing requirements, and shrinkage assumptions.

The model consolidated forecasted ticket volumes, handling time, planned headcount, absenteeism, breaks, and in-office shrinkage to calculate required productive hours and identify real-time and forward-looking coverage gaps.

Operations and leadership teams used outputs to monitor SLA risk, staffing efficiency, and intraday performance, enabling data-driven scheduling adjustments and improved resource allocation.

This work supported consistent service coverage across all operating hours while improving visibility into utilization, staffing shortfalls, and operational risks.

I built a Power BI reporting solution to monitor quality performance across support operations, consolidating weekly accuracy metrics, correct cases, mistaken cases, and unconfirmed outcomes into a single operational view.

The dashboard provided leadership and quality teams with clear visibility into global accuracy trends, weekly performance fluctuations, and volume distribution, enabling early identification of quality risks and performance gaps.

Interactive visuals were designed to support drill-down analysis by time period and operational queue, allowing stakeholders to track progress, compare performance over time, and prioritize corrective actions.

This reporting solution improved transparency, reduced manual reporting effort, and supported continuous quality improvement initiatives across the operation.