vraagvoorspelling

vraagvoorspelling is the process of estimating future demand for goods or services over a defined time horizon. It combines historical data, market information and other relevant inputs to provide a forecast that supports decision making in operations, supply chain and finance. The goal is to balance service levels with costs by anticipating how much is needed, when and where.

Typical methods fall into quantitative and qualitative approaches. Quantitative methods include time-series models such as ARIMA and exponential smoothing, causal models that link demand to drivers (prices, promotions, economic indicators), and increasingly, machine learning techniques (random forests, gradient boosting, neural networks) that can handle complex patterns. Qualitative methods rely on expert judgment, such as the Delphi method or consensus meetings, especially when data are sparse or rapidly changing. Many organizations use hybrid approaches that combine multiple models and expert input.

Inputs to vraagvoorspelling often cover historical sales, price changes, promotions, seasonality, macroeconomic trends, consumer behavior, weather, and planned marketing activities. Forecast outputs usually comprise point estimates and prediction intervals to express uncertainty, with granularity by product, region, channel and time period.

Key concepts include forecast horizon, forecast accuracy and bias, and performance evaluation using metrics such as MAE, RMSE or MAPE. Forecasts feed processes like inventory management, procurement, capacity planning, and workforce scheduling, facilitating risk-aware decisions.

Challenges include data quality, model selection, structural changes, and unforeseen events (economic shifts, supply disruptions). Ongoing monitoring, scenario planning and rolling forecasts help maintain relevance and reliability. Related topics include demand planning and supply chain forecasting.