Forecasting Demand Under Limited Data: Benchmark Models for Workforce Capacity Planning in Translation Services

Demand variability complicates workforce capacity planning in knowledge-intensive services, particularly when only short historical records are available. This study evaluates parsimonious benchmark models for forecasting translation-service demand and translating the resulting forecasts into staffing requirements. The empirical analysis used 122 daily observations collected over six months from a translation team providing services in English and Spanish. Demand was measured as the number of words requested per working day, while nominal individual capacity was operationalized as 2000 translated words per day. Three benchmark forecasting methods—Naive, Mean, and Drift—were compared through expanding-window rolling-origin evaluation using ME, MAE, RMSE, MASE, and sMAPE. At the one-working-day horizon, the Mean benchmark achieved the lowest MAE (3221.66 words), RMSE (4424.81 words), MASE (0.845), and sMAPE (58.14%), and it maintained the lowest values for these measures at five- and twenty-working-day horizons. Re-estimated using all 122 observations, the Mean benchmark generated a point forecast of 5472.12 words per working day, equivalent to 2.74 translator-equivalents and a baseline requirement of three translators under the nominal productivity assumption. However, observed demand exceeded three-translator capacity on 34.43% of working days, indicating the need for flexible contingency capacity. The study provides a transparent framework connecting benchmark forecast evaluation with workforce-capacity decisions under limited temporal coverage.