Which method of forecasting is primarily utilized for understanding demand patterns over time?

Time-series forecasting is the method primarily utilized for understanding demand patterns over time because it analyzes historical data to identify trends, seasonal patterns, and cycles. This technique assumes that past demand patterns will continue into the future, which allows businesses to make informed predictions about future demand based on this historical data.

By using time-series analysis, organizations can effectively capture variations in demand due to time-dependent factors such as seasonality (e.g., holiday shopping) or trends (e.g., increasing demand for a product over several years). This method is particularly beneficial for numerous industries that rely on capturing time-dependent fluctuations in demand, enabling more effective planning and inventory management.

In contrast, qualitative forecasting relies on expert judgment and subjective assessment, simulations assess complex scenarios but do not focus solely on demand over time, and benchmarking involves comparing processes or performance metrics against industry standards rather than analyzing past demand patterns. Thus, time-series forecasting is the most suitable method for identifying and analyzing demand trends over time.