Recognizing AI Capabilities Across the SAP IBP Planning Process

Within the IBP stack, we follow a typical process flow: starting with integrating and checking master data from the ERP system into IBP. Based on this data, we build a demand plan, which becomes the input for the inventory plan, leading to the creation of unconstrained or constrained supply plans.

Over time, SAP IBP licenses have embedded various AI capabilities, including classical machine learning and narrow AI. For example, when integrating and checking master data, machine learning assists with pattern recognition of incoming master data and helps to automatically check new data from ERP. In the demand module, extensive AI is implemented, particularly in pre-processing steps and machine learning algorithms for demand forecasting.

Since last year, we have invested heavily in bringing more generative AI capabilities into IBP, including both embedded generative AI and enhancements of Joule in IBP. This year's focus was on three areas:

1. Enhancing features and capabilities of Joule, especially transactional features such as starting or scheduling jobs, sharing content or running master data consistency checks directly via Joule.

2. Explainability, making results from demand forecasts and inventory planning more approachable and naturally understandable via generative AI.

3. Scenario management, enabling more complex workflows in Joule.

• Joule Orchestrates AI intelligently connecting and managing AI capabilities as a central agent, automating supply chain planning workflows and transactional tasks across IBP.
• Embedded AI Delivers Performance integrating machine learning directly into demand forecasting, inventory optimization, and master data validation for immediate planning improvements.

• Explainability and Scenarios enable generative AI to make planning results naturally understandable and support complex scenario management for resilient, data-driven supply chain decisions.