Business Pain Points
The use of different algorithmic models for each link of supply chain intelligence (procurement/warehousing/logistics) leads to cumulative amplification of prediction bias.
Collaborative Program
- data federation
- Adoption of the A2A
ArtifactMechanisms to exchange forecast intermediate results (e.g., warehouse intelligences to provide inventory turnover matrices) - utilization
content.partsTransfer of structured data tables (CSV/Parquet format)
- Adoption of the A2A
- model relay
- Procurement of Smart Body Launch
task_type: "demand_forecast"joint mission - Logistics Intelligence returns with additional transportation time factor
task_update
- Procurement of Smart Body Launch
Implementation steps
- extensions
A2AServerclass implements the data validation interface (validate_input_schema) - configure
TaskRoutingPolicyEnabling dynamic selection of intelligences (e.g. prioritizing calls to the prediction module of the SAP system) - pass (a bill or inspection etc)
aggregationMethodField definition result aggregation algorithms (weighted average/neural network, etc.)
Effectiveness Verification
After the pilot enterprise applied the program, the forecasting accuracy increased by 281 TP3T and the inventory turnover days decreased by 191 TP3T.
This answer comes from the articleA2A: Google releases open protocol for communication between AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to Improve Demand Forecasting Accuracy of Supply Chain Intelligence through A2A?
