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Monthly Forecasting

Monthly forecasting estimates demand, workload, revenue, or staffing needs for each month in a future planning period. Operations teams use it for budgets, hiring, seasonal capacity, vendor plans, and other decisions that need more lead time than a weekly schedule.

A monthly forecast is a planning layer, not a finished staff schedule. It can show that demand will rise in November, but it does not show whether the peak arrives on Monday mornings or during the final week. Teams must split the monthly total into weeks, days, and intervals before they calculate shift coverage.

A simple monthly forecasting method

  1. Collect at least one complete seasonal cycle when it is available, and keep the same demand definition for every month.
  2. Build a baseline with a moving average, seasonal index, or another method that fits the history.
  3. Add known drivers such as campaigns, launches, holidays, opening-hour changes, or customer growth.
  4. Create a base case and a reasonable high and low case instead of relying on one exact total.
  5. Compare the forecast with actual demand each month. Track WAPE for error size and bias for repeated over- or under-forecasting.

Monthly forecast formula

A simple seasonal forecast can use: monthly forecast = baseline monthly demand ร— seasonal index + known adjustments. The method is easy to review, but the inputs must use comparable months. A structural business change can make older history less useful.

Why this matters for planners and team leads

Monthly forecasting gives planners time to change capacity before demand arrives. It can support hiring, leave limits, cross-training, outsourced capacity, overtime budgets, and campaign planning. It also gives finance and operations one demand view for the same period.

Do not judge a monthly forecast only by the annual total. Equal over- and under-forecasts can cancel each other across the year while individual months still create serious staffing gaps. Review error and bias by month, work type, and location.

Example in practice

A support team handled 10,000 tickets in a typical month. Historical data shows that November demand is usually 20% above the baseline, and a planned launch is expected to add another 1,500 tickets. The initial November forecast is 10,000 ร— 1.20 + 1,500 = 13,500 tickets.

The planner then divides those tickets across weeks and days using the operation's normal arrival pattern. Average handling time converts the demand into workload hours. Shrinkage, skills, and service targets convert the workload into staffing requirements that can be used in the schedule.

Put this into practice

See how Soon handles monthly forecasting in your shift scheduling workflow.

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Workforce forecasting softwareHow to forecast staffing needs