• To develop quantitative, judgmental, and causal forecasting models for seasonal, intermittent, and new product demand.
• How to choose the right forecasting method for each data pattern.
• How to improve forecast performance with Machine Learning and Lean Six Sigma principles.
• To assess forecast performance based on forecast precision, forecast accuracy and forecastability
• How to mitigate the risk of inaccurate forecasts and deal with randomness, low forecastability, missing data, outliers, disruptive events, and overfitting.

Section 2.1. Forecasting myths, realities and challenges

2.1.1. Forecasting myth 1 - About predicting the future

2.1.2. Forecasting myth 2 - About choosing a forecasting model

2.1.3. Forecasting myth 3 - About model fitting

2.1.4. Forecasting myth 4 - About model sophistication

2.1.5. Forecasting myth 5 - About About Artificial Intelligence

2.1.6. Forecasting myth 6 - About modeling data

Section 2.2. Forecasting based on historical data

2.2.1. The role of historical data in forecasting

2.2.2. Missing data, events and outliers

2.2.3. Naïve and Moving Average Methods

2.2.4. Exponential smoothing and linear regression

2.2.5. ARIMA models

Section 2.3. Seasonal demand forecasting

2.3.1. Decomposition method

2.3.2. Decomposition method example

2.3.3. Holt-Winters method

2.3.4. Holt-Winters method example

Section 2.4. Intermittent demand forecasting

2.4.1. Croston method

Section 2.5. Judgmental and causal forecasting models

2.5.1. Judgmental forecasting models

2.5.2. Causal forecasting models

Section 2.6. Forecasting with Machine Learning

2.6.1. Machine learning fundamentals

2.6.2. Decision trees in machine learning

2.6.3. Machine learning example

Section 2.7. New product forecasting

2.7.1. New product forecasting fundamentals

2.7.2. Assumptions-based modeling and scenario analysis

2.7.3. Quantitative analysis to forecast new product demand

Section 2.8. Forecast performance

2.8.1. Forecast error

2.8.2. Impact of aggregation on forecast accuracy

2.8.3. Forecast accuracy and forecastability

Section 2.9. Impact of randomness and disruptive events in forecasts

2.9.1. Understanding randomness and disruptive events

2.9.2. Mitigating risk

Section 2.10. Improving forecasting with Lean and Six Sigma Principles

2.10.1. Forecast Value-Added Analysis

2.10.2. Applying Six Sigma methodologies to forecasting