AI agent that monitors supply chain signals, detects disruption risks, and recommends re-routing decisions — powered by Vertex AI and BigQuery ML on real-time logistics data. From reactive to predictive in weeks.
We implement a production-grade supply chain intelligence agent on Google Cloud — ingesting real-time logistics and supplier data via Cloud Dataflow, scoring risks continuously with BigQuery ML, and surfacing the Gemini 2.0-powered agent to your operations team via a dashboard or Slack integration. Approved re-routing actions can trigger downstream procurement workflows automatically.
Supply chain disruptions — supplier failures, port delays, weather events — are often identified only after they have already impacted operations, when recovery is expensive.
Operations teams manually monitor supplier health, news, and logistics signals across large supplier networks — a time-consuming process that misses early warning signals.
Without predictive intelligence, organisations respond to supply chain failures rather than preventing them — accepting higher costs, stockouts, and customer service failures.
BigQuery ML models continuously score every supplier against financial health, delivery performance, news signals, and geopolitical risk — updated in real time via Pub/Sub.
The agent correlates weather data, logistics signals, geopolitical events, and supplier patterns to predict disruptions up to 14 days before they impact your operations.
When disruption risk is detected, the agent generates ranked re-routing options — alternative suppliers, logistics routes, safety stock draw-down — with cost-benefit analysis.
Approved re-routing decisions can trigger procurement actions directly — purchase order amendments, supplier notifications, and logistics bookings via integrated APIs.
Risk visibility extends beyond Tier 1 suppliers to Tier 2 and Tier 3 — giving a complete picture of the supply chain dependency network and hidden concentration risks.
The agent ingests internal data (purchase orders, goods receipts, delivery performance history) alongside external signals: news feeds, weather APIs, maritime and logistics tracking data, commodity price feeds, and supplier financial data. During implementation we configure the data sources most relevant to your specific supply chain risk profile and industry.
Tier 2 and Tier 3 visibility is built from your supplier-provided supply chain declarations, industry databases, and BigQuery ML inference based on product category and origin patterns. We work with you during discovery to map your known supplier network and identify where multi-tier data is available versus where it must be estimated from proxy signals.
The agent operates within a configurable approval framework. Low-risk, pre-approved actions (e.g., activating a backup supplier within an existing contract) can be executed autonomously. Higher-risk actions (e.g., a new supplier engagement above a cost threshold) require human approval. The approval boundary is fully configurable during implementation to match your procurement governance policy.
A standard Supply Chain Intelligence Agent implementation runs 6–8 weeks: 2 weeks for data source integration and BigQuery ML model training, 2 weeks for agent logic and routing recommendation engine, 2 weeks for integration testing with your procurement and ERP systems, and 2 weeks of supervised operation before full handover. Timelines vary based on the number of data source integrations required.
Stop reacting to supply chain failures. Predict and prevent them before they impact operations. We implement in 6–8 weeks.