Long-range staff sizing and short-term capacity optimization system

Engineered a multi-layer workforce optimization engine combining demand forecasting, sales-per-hour models, staffing requirement estimation, regulatory constraints, and capacity allocation alongside long-range hiring plans and weekly scheduling recommendations.

AI operated at both the planning and directive layers, estimating required capacity while recommending workforce allocation, prioritizing critical locations, and balancing service levels, labor costs, and operational constraints before execution.

Reliability was engineered explicitly. Minimum staffing levels were formalized. Labor regulations and allocation constraints were encoded. Every recommendation was traceable to demand forecasts, productivity assumptions, capacity gaps, and operational priorities. Simulations stress-tested staffing plans before release. Authority boundaries between AI recommendation, workforce approval, and human override were clearly defined. Any failure could create service disruption or excessive labor costs.

For Process: The system was embedded directly into workforce planning, hiring forecasts, weekly scheduling, and branch-level capacity allocation. For People: The system was designed to support workforce planners, operations teams, branch managers, and HR leaders while preserving managerial oversight. For Data: The system unified sales, demand, attendance, productivity, staffing, and scheduling data, with corrective mechanisms against incomplete or inconsistent inputs.