Operations - Demand Forecasting & Scheduling
Stabilize service levels and reduce overtime.
The Challenge
Operations teams struggle with unpredictable demand, leading to either costly overtime or poor service levels when understaffed. While historical data exists, it's not being used systematically to predict busy periods and optimize scheduling.
The Solution
AI-powered demand forecasting that analyzes historical patterns, seasonal trends, and external factors to predict staffing needs. The system provides scheduling recommendations that help balance service levels with cost control, reducing both overtime and understaffing.
Signals it's a fit
Unpredictable staffing needs leading to overtime or understaffing
Historical demand patterns exist but aren't systematically used
A small pilot team is willing to try data-driven scheduling
Expected Impact
Reduced overtime
More predictable service levels
How we approach this
Collect historical demand and staffing data
Build simple, explainable forecasts
Align schedules with predicted peaks
Risks & Considerations
Poor data quality
Seasonality not captured