In the grocery business, reducing waste while maintaining availability has become one of the most complex operational challenges retailers face today.

To tackle waste in fresh food, we must close the gap between "commercial priority" and "physical reality." Kaleris achieves this by fusing TMS Priority Flags with IoT and AI/ML within a Digital Twin of the yard to drive a risk-based execution strategy.

The process begins with the TMS, which leverages specific Priority Flags on inbound Purchase Orders. This allows the system to identify high stakes loads, like fresh produce or prepared foods, before they even arrive. While the TMS focuses on the transport, it passes this "High Priority" status directly to the YMS, ensuring the yard understands the urgency of the arriving asset.

Once the truck enters the gate, IoT takes over to protect the quality. Our yard sensors actively monitor reefer units in real-time, streaming critical temperature and fuel data into the Digital Twin.

Here is where AI and Machine Learning drive the value. Our AI models analyze the combined data stream—the Priority Flag from the TMS plus the real-time IoT health data of the trailer. If the AI detects a "High Priority" load that is also flagging a temperature variance or low fuel, it autonomously assigns a "Critical Risk" score. The system then overrides the standard FIFO schedule, triggering a "hot move" task to the yard jockey.

This is predictive execution. By using AI to prioritize moves based on the assigned value and physical condition of the asset, we ensure that the most vulnerable inventory is docked first. This enables grocers to achieve First-Expiring, First-Out (FEFO) outcomes, reducing waste, and ensuring that "rapid delivery" is backed by actual on-shelf availability.