Discover published sets by community

Technique Description: Utilizes historical data to predict future demand by identifying patterns. When to Use: Stable environments with abundant historical data.

Technique Description: Establishes relationships between dependent and independent variables to predict demand. When to Use: When data relationships and trends need to be quantified.

Technique Description: A structured communication technique that relies on a panel of experts. When to Use: Scenarios where expert qualitative judgments are needed, especially for long-term forecasting.

Technique Description: A weighted moving average method that reduces the weight for older data points. When to Use: When smoother, less erratic forecasts are needed and quick adaptations to recent changes.

Technique Description: Forecasts demand based on the average of a specific number of the most recent observations. When to Use: Simple scenarios when trends and seasonal patterns are not present.

Technique Description: Breaks down historical data into trend, seasonal, and cyclical components. When to Use: When data exhibits strong seasonal or cyclical patterns over time.

Technique Description: A sophisticated statistical approach using autoregression and moving averages. When to Use: When handling complex time series that incorporate both auto-correlation and non-stationarity.

Technique Description: Uses subjective opinions rather than just hard data. When to Use: When insufficient historical data is available or rapid changes occur in the market.

Technique Description: Machine learning algorithms modeled after the human brain that can learn from data. When to Use: Highly complex, nonlinear and dynamic environments where relationships between data points are not clearly understood.

Technique Description: A statistical technique for intermittent demand forecasting. When to Use: Specially useful for spare parts demand prediction or any scenario with irregular demand.

Technique Description: Uses randomness to solve problems that might be deterministic in principle. When to Use: When you need to understand the impact of risk and uncertainty in prediction and forecasting models.

Technique Description: Forecasting that relies on expert opinions and market research rather than numerical data. When to Use: When numerical data is scarce or the focus is on new products and innovations.

Technique Description: Uses economic theories to model and predict future behaviors. When to Use: When you need to predict long-term demand based on economic indicators and relationships.

Technique Description: Looks at cause-and-effect relationships to predict demand, such as the effect of marketing campaigns or price changes. When to Use: When specific factors known to influence demand can be quantified.