Explainable AI for Consumer Goods and Retail Demand Forecasting
How transparent analytics establish trust and increase forecast adoption for reliable, efficient demand planning
About the webinar
Often the biggest challenge with forecasting is adoption – incorporating forecasts into decision making.
This is frequently due to a lack of trust and the inability to answer the question: Why has the forecast changed?
Transparency into the underlying drivers of demand, such as competitive pressures, promotional tactics and economic effects, boosts trust in the business and reduces the need for forecast overrides.
In this webinar, SAS forecasting experts explore the importance of explainability for translating forecasts into useful components.
What you'll learn:
- Practical explainability techniques across time series and machine learning models.
- Key considerations for consuming explainability results and for using data efficiently.
- A forecast explainability framework for consumer goods and retail.