How can you measure and improve forecast accuracy for daily specials and promotions?

• A. Compare actual sales to forecast and adjust using only seasonal trends.
• B. Ignore promotions when forecasting.
• C. Use a simple forecast and rarely adjust.
• D. Compare actual sales to forecast, calculate forecast error; adjust forecasting model using historical data and seasonality, event calendars; incorporate promotions and kitchen capacity.

Answer: (D)

Measuring forecast accuracy comes from comparing what you predicted for daily specials and promotions with what actually happened, then using that difference to improve. Track actual sales against forecast each day and compute the forecast error (the gap between predicted and actual). Using a clear error measure, like average error or a percentage error, gives you a concrete signal of how far off you are and how this changes over time.

The strongest approach uses that error signal to refine the forecasting model. Build forecasts from historical data and recognize patterns such as day-of-week and seasonal trends, but also attach importance to event calendars, promotions, and marketing pushes—these often cause spikes that regular seasonal patterns miss. Explicitly include promotions as part of the forecast logic (for example, dummy indicators for promo days or uplift factors tied to promotion types) so you can anticipate their impact. Also factor in kitchen capacity, since production limits can cap how much you can sell or produce, and this helps prevent over- or under-forecasting. With a loop of measuring actual versus forecast, calculating errors, and adjusting the model to reflect history, seasonality, events, promotions, and capacity, forecast accuracy improves over time.

The other options fall short because they omit promotions, rely only on seasonal patterns, or keep forecasts static with little adjustment, which leads to persistent inaccuracies when promotions or capacity constraints arise.