Which forecasting technique uses historical data to predict future demand?

Time series analysis is a quantitative forecasting technique that specifically relies on historical data to identify patterns, trends, and seasonality in order to predict future demand. By analyzing past data points collected over specific time intervals, time series analysis can discern recurring patterns and fluctuations, thereby facilitating more accurate forecasting by projecting these observed trends into the future.

This method is particularly useful for supply chain management as it enables organizations to make informed decisions regarding inventory levels, production schedules, and resource allocation based on anticipated demand based on historical performance. The statistical techniques employed in time series analysis, such as moving averages or exponential smoothing, help to refine these predictions further.

Qualitative forecasting relies more on subjective judgments or expert opinions rather than explicit historical data, which is not suitable for this context. Market research typically involves gathering real-time data regarding consumer preferences and market conditions rather than using historical trends, while trend analysis focuses on identifying general directions in data but may not utilize the systematic time-dependent approach characteristic of time series analysis.