Voice-based operation:
Formulate planning goals directly in natural language – as an additional way to access SAP TM.
Rethinking SAP TM – AI agents plan, optimize, and understand language
How can experiential knowledge be systematically leveraged in SAP TM?
SAP TM is the powerful foundation for professional transport management – and there’s even more to this foundation. Route planning, rescheduling, and operational adjustments currently run reliably based on rules. However, the experiential knowledge that dispatchers contribute with every manual intervention has not, until now, automatically fed back into future planning. This is exactly where abat’s AI Agents come in: They enhance SAP TM with adaptive optimization and voice control – directly within the system.
How do AI agents work in SAP TM?
abat integrates AI Agents directly into SAP TM to close this gap:
- Natural interaction: Schedulers use text input to describe what they want to achieve – such as alternative routes, rescheduling, or prioritization – and the AI Agents automatically implement the changes in SAP TM.
- Learning-based optimization: The agents use historical transport and rescheduling data, learn from previous manual decisions, and optimize schedules dynamically and based on data rather than fixed rules.
- Flexible operation: Can be used as a chat interface for day-to-day operations or as an automated optimization agent running in the background – fully operable on-premises.
This systematically harnesses experiential knowledge and applies it automatically. Your SAP TM thus gains a voice-based user interface, becomes adaptive, and operates even more efficiently.
Advantages and benefits
Learning-based route planning:
Continuous improvement based on historical rescheduling.
Leveraging empirical knowledge:
Knowledge gained from manual interventions is systematically captured and applied automatically.
Greater efficiency:
Less effort per rescheduling, adaptive optimization during ongoing operations.
Secure and integrated:
On-premises capable and directly integrated into existing SAP TM processes – even for sensitive system landscapes.
Bring language to your transportation planning
What could your scheduling team achieve if rescheduling were simply described rather than configured? Talk to us – we’ll use your SAP TM processes as an example to show you how AI Agents plan, optimize, and understand language.
AI Agents in SAP TM are a typical AI-Shore use case: AI that works directly within your system – on-premises and integrated, rather than as a standalone solution. With AI- Shore, we develop and operate such agents in compliance with data protection regulations within your system landscape
Frequently asked questions about AI agents in SAP TM
How does abat work with AI agents?
abat integrates AI agents directly into SAP Transportation Management (SAP TM). Dispatchers describe in natural language what they want to achieve – such as rescheduling or prioritization – and the agents implement the changes in the system. In addition, they learn from historical rescheduling data and continuously improve route planning. Fully operable on-premises.
Do the AI agents replace our dispatchers?
What do the agents learn from?
Can the agents be operated on-premises?
Does our SAP TM need to be modified for this?
Contact our AI experts
Europe
China
North America
