Applying AI in Demand and Inventory Planning

Exploring Artificial Intelligence in Demand Planning Forecast Explainability via GenAI

Objective

After completing this lesson, you will be able to understand the use of generative AI for forecast explainability in SAP Integrated Business Planning for Supply Chain.

Forecast Explainability

Statistical forecasts can feel like a 'black box.' When asked to justify a number, how do you explain the 'why' behind the system's calculation with confidence and clarity?

Explainable forecasting interface showing an “Explain” panel that documents preprocessing (e.g., outlier correction), recommends a model, and justifies the best-fit choice—Automated Exponential Smoothing—over alternatives using error metrics, for transparency and auditability.

To increase transparency, forecast explainability features using generative AI are available:

  • Detailed drilldowns are possible directly from statistical forecast values, showing numeric KPIs, preprocessing steps, algorithms considered, and their errors.

  • Natural language summaries explain how forecasts were derived and provide recommendations for further improvement.

  • These features are available in both the Planner workspace and Excel add-in.

Lesson Summary

  • Transparent Forecast uses generative AI to transform statistical forecasts from a "black box" into explainable insights, showing numeric KPIs, preprocessing steps, algorithms considered, and error metrics with full auditability.
  • Natural Language automatically summarize how forecasts were derived, identify the best-fit algorithm, and recommend improvements such as alternative preprocessing steps to optimize demand forecast accuracy.
  • Accessible Across All Platforms delivers forecast explainability in both the Planner Workspace and Excel add-in, enabling transparent decision-making without requiring statistical expertise.
Learning

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