Zansa › Answers › Forecasting and data science
How can data help forecast staffing and rostering needs?
Forecast the workload first, such as customer arrivals, orders, bookings or calls by hour and day, then convert that workload into the staff needed using productivity rates. Compare the resulting requirement with actual rosters to find over- and under-staffing. This keeps labour cost and service levels in balance.
Workload forecasts usually need a finer time grain than sales forecasts, often hourly or by shift, and they depend strongly on day of week, holidays, weather, events and promotions.
The conversion step matters as much as the forecast: how many orders can one person process per hour, what minimum cover is needed, and what labour rules apply. Build in a buffer for sickness and breaks, and have managers review the output. A forecast informs a roster; it should not override legal limits or fairness rules.
Want this applied to your business? Zansa builds dashboards and forecasts for small and mid-sized companies worldwide. Start with a free intro call.
Book a call