Demand Forecasting Model That Reduced Overstaffing Costs
The Challenge
The operations team was relying on prior-year actuals and gut instinct for staffing decisions. With no forward-looking model, they consistently overstaffed in slow periods and scrambled to cover peaks — both of which affected margin and service quality.
What We Did
We built a statistical demand forecasting model using booking data, historical occupancy patterns, and leading indicators specific to their business. The model was integrated into a planning dashboard that operations managers could run weekly without analytical support.
Results
Forecast accuracy within 8% of actuals
across the first full quarter of deployment
Staffing decisions shifted from reactive to proactive
with a 6-week planning horizon
Reduced reliance on analyst support for planning cycles
ops managers run the model independently
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