I have over five years of experience in workforce capacity planning, where I analyzed historical data, forecasted demand, and optimized staffing levels to meet operational goals. My expertise includes using tools like Excel, Tableau, and workforce management software to create accurate models. In this role, I would leverage my analytical skills to ensure the right number of staff is scheduled at the right times, minimizing costs while maximizing efficiency. My ability to collaborate with operations and HR teams ensures alignment with business objectives.

I use a combination of historical data, trend analysis, and predictive modeling to forecast staffing needs. By identifying seasonal patterns, peak hours, and business growth trends, I create flexible staffing plans. I also collaborate with department heads to understand upcoming projects or promotions that may impact demand. This proactive approach ensures we are prepared for fluctuations without overstaffing or understaffing, maintaining service levels and employee satisfaction.

Key metrics I prioritize include occupancy rate, service level, adherence to schedule, and shrinkage. Occupancy rate helps assess productivity, while service level measures customer satisfaction. Adherence ensures employees follow schedules, and shrinkage accounts for unavoidable absences. By monitoring these metrics, I identify gaps and adjust plans accordingly. Balancing these KPIs ensures optimal performance and cost-efficiency while meeting business targets.

I address unexpected shortages or surges by maintaining a pool of part-time or on-call staff and cross-training employees for flexibility. I also use real-time monitoring tools to detect deviations from forecasts early. If a surge occurs, I adjust schedules or offer overtime, while shortages may require reallocating resources or leveraging temporary staffing. My goal is to minimize disruption while keeping labor costs in check.

At my previous job, I identified a recurring understaffing issue during peak hours, leading to long wait times. By analyzing call volume patterns, I redesigned shift schedules to align with demand spikes. This reduced average handle time by 15% and improved customer satisfaction scores by 20%. The change also lowered overtime costs, demonstrating how data-driven planning enhances both efficiency and cost savings.