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Forecasting Using Excel

Forecasting in Excel uses spreadsheet formulas, tables, and charts to estimate future demand from historical data. It can work well for a small operation with a stable demand pattern, one model owner, and a planning cycle that does not need frequent automatic updates.

The spreadsheet should separate raw actuals, cleaned history, assumptions, the baseline forecast, manual adjustments, and accuracy results. This structure makes the model easier to audit and reduces the risk that a copied formula or overwritten value silently changes the forecast.

How to build a demand forecast in Excel

  1. Put one demand measure and one time interval in each row. Keep dates, queues, channels, and locations in separate columns.
  2. Mark missing data, outages, one-time events, and other anomalies. Do not delete real peaks that the operation may see again.
  3. Create a simple baseline, such as the average of comparable prior periods or a seasonal moving average.
  4. Keep event and management adjustments in separate columns with an owner and reason.
  5. Hold back recent periods for testing. Compare each method on data it did not use to fit the forecast.
  6. Track WAPE and bias, then use the method that performs best at the interval used for staffing.

Basic spreadsheet formulas

A simple baseline can use: forecast = average demand from comparable periods ร— seasonal index + known event adjustment. Staffing then uses: workload hours = forecast demand ร— average task minutes รท 60. Add occupancy, shrinkage, service targets, and skills before the result becomes a staff schedule.

Why this matters for planners and team leads

Excel is useful because planners can inspect every assumption and start without a software project. It becomes risky when several people edit copies, data must refresh every day, many queues need separate models, or the workbook connects to staffing and schedule decisions at short intervals.

Move to forecasting software when maintaining the workbook takes more time than reviewing the forecast, when formula errors are hard to detect, or when teams cannot reproduce which data and assumptions created the published plan.

Example in practice

A team forecasts Monday ticket demand with the average of the previous eight comparable Mondays. It keeps holiday weeks out of the normal baseline, then adds a separate adjustment for a planned campaign. The workbook compares the result with actual tickets and calculates WAPE and bias.

This is manageable for one queue. If the team must repeat the process for many queues, 15-minute intervals, several seasonal patterns, and daily updates, automatic data connections and model backtesting become more reliable than copied spreadsheet tabs.

Put this into practice

See how Soon handles forecasting using excel in your shift scheduling workflow.

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Workforce forecasting softwareHow to forecast staffing needs