What is a common technique used in demand forecasting?

Time series analysis is a widely used technique in demand forecasting because it relies on historical data to identify patterns and trends over time. This method analyzes past demand data collected at regular intervals, such as daily, weekly, or monthly, to predict future demand. By examining these patterns, such as seasonality or cyclical behavior, businesses can make more informed decisions about inventory levels, production scheduling, and resource allocation.

Time series analysis involves statistical methods and models that can account for various factors influencing demand. This technique is particularly valuable when there is a significant amount of historical data available and the demand shows consistent behavior over time. By leveraging past trends, organizations can generate accurate forecasts, helping to align supply with anticipated customer needs.

The other techniques listed each serve different purposes and may not focus primarily on forecasting demand. Qualitative analysis relies on subjective judgment and insights, making it more appropriate for scenarios where quantitative data is lacking. Random sampling can be used for data collection but does not directly provide a systematic approach to forecasting. SWOT analysis is a strategic planning tool that assesses strengths, weaknesses, opportunities, and threats but is not specifically designed for demand prediction.