Smarter Supply Chains with Agentic AI: Forecasting, Documents and Real-Time Decisions

Modern supply chains operate in high-stakes environments where disruptions are constant and margins for error are razor-thin. Whether it’s delayed shipments, fluctuating demand, or paperwork bottlenecks, operations teams are under pressure to act faster and plan better.

But faster isn’t always smarter.

Today’s logistics leaders need systems that can think ahead, respond in real time, and eliminate manual inefficiencies. That’s where agentic AI steps in. At 0to60.ai, we’ve combined real-time data pipelines with autonomous AI agents to help organizations move from reactive planning to intelligent orchestration.

In this article, we walk through how agentic AI — powered by 0to60.ai and Databricks — is transforming demand forecasting and document processing in real supply chain settings.

Despite digital advancements, many supply chains are still held back by two critical issues:

1. Unreliable forecasting: Static demand models often fail to reflect current realities like supplier constraints, lead time variability, or market shifts. As a result, teams deal with overstocking, missed orders, and constant firefighting.

2. Manual document handling: Contracts, invoices, and bills of lading are processed manually or semi-automatically, leading to slow ERP updates, miscommunication, and errors in fulfillment and reconciliation.

The lack of real-time coordination across systems like ERP, WMS, and procurement platforms creates visibility gaps that cost both time and money.

We implemented a dual-agent solution to tackle both forecasting and document intelligence — built using 0to60.ai’s orchestration engine and Databricks’ data infrastructure.

Autonomous Planning Agents

Planning agents are configured to:

• Forecast demand using historical trends, live POS signals, supplier performance, and external events
  
• Adjust procurement recommendations based on stock levels, supplier delivery times, and demand shifts
  
• Trigger alerts when a potential disruption is detected (e.g., shipment delays, demand spikes, or low stock coverage)
  
  

These agents operate continuously, refining forecasts as new data flows in and flagging anomalies before they cause disruptions.

Document Extraction Agents

To reduce bottlenecks in order processing and reconciliation, we deployed document agents that:

• Extract structured data from contracts, invoices, and bills of lading using Agent Bricks
  
• Apply vector search to match shipping terms, payment conditions, and real-time logistics data
  
• Automatically update ERP and WMS systems with clean, validated data
  
  

This eliminated repetitive tasks like copying invoice fields into ERP forms or manually reviewing supplier contracts for clause compliance.

Behind the scenes, the solution runs on a seamless integration of 0to60.ai’s agent orchestration framework and Databricks’ real-time data infrastructure.

• Agent Bricks power the forecasting and document extraction logic‍
• Databricks Lakehouse ingests and prepares historical and live data‍
• Confluent Kafka streams supply chain events (shipment updates, stock signals, etc.) into the agent workflows

• 0to60.ai orchestrates real-time decision-making, integrates with ERP and WMS platforms, and routes alerts to planning teams when human review is needed
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The result is a supply chain that runs on real-time data, proactive alerts, and intelligent automation.