AI Agentic Workflow for Lot and Serial Number Tracking

Manual lot/serial number capture at receiving, picking, and shipping is slow and error-prone. A custom workflow automates this via fixed-mount or robot-mounted vision scanners integrated directly with the WMS. This eliminates a dedicated data-entry role, reduces touch labor by 70-90%, and ensures 99.9%+ data accuracy from the moment goods enter the facility.

During a product recall, manually tracing affected lots through spreadsheets and paper trails can take days, increasing liability and waste. An automated tracking workflow maintains a real-time, queryable chain of custody. When a lot is flagged, the system instantly identifies all associated inventory locations (shelf, pallet, packed order) and can automatically lock them in the WMS, slashing containment time from hours to minutes.

Preparing for FDA, EMA, or other regulatory audits requires manually assembling proof of chain-of-custody, which is labor-intensive and risky. A custom workflow automatically logs every lot movement with timestamp, user/system ID, and location, generating audit-ready reports on demand. This cuts prep time from 2-3 weeks to 1-2 days and provides a defensible, digital audit trail that satisfies inspector requirements.

In pharma, food, and chemicals, manually managing First-Expired-First-Out (FEFO) or First-In-First-Out (FIFO) leads to costly spoilage. The tracking workflow integrates lot/expiry data directly into pick-path logic within the WMS or WCS. The system automatically directs pickers or robots to the correct lot, preventing expired goods from being shipped and reducing write-offs by 15-25%.

In B2B sectors like aerospace or medical devices, customers demand granular proof of provenance. This automated tracking system can expose authenticated lot history via a customer portal or API, turning a compliance function into a value-added service. This strengthens customer relationships, supports premium pricing, and differentiates your brand as a leader in supply chain transparency.

Granular lot/serial data, when clean and automated, becomes a high-value asset. It enables advanced analytics on supplier quality (linking defects to specific lots), warehouse efficiency (tracking lot velocity), and warranty management. The custom workflow ensures this data is structured and reliable from the start, providing the foundation for predictive models that drive further cost savings and quality improvements.

Specialized agents orchestrate the capture of lot/serial data via fixed-mount scanners, mobile computers, or vision systems at receiving, picking, and shipping. They validate scanned data against Advance Shipment Notices (ASNs) and purchase orders in real-time, flagging mismatches for immediate quarantine. This eliminates manual key-entry errors and ensures the digital record matches the physical item from the moment it enters the facility.

The core workflow engine, built on frameworks like LangGraph, maintains a temporal graph of every touchpoint for each tracked unit. It logs agents (human or robotic), locations, timestamps, and transactions (e.g., 'split from lot A to serials B&C'), creating an immutable, queryable audit trail. This granular history is critical for regulated recalls, warranty claims, and proving compliance during audits.

The workflow is not a standalone system. It deeply integrates via APIs with the Warehouse Management System (WMS) to attach lot/serial data to every inventory transaction (putaway, pick, pack). Simultaneously, it pushes verified chain-of-custody events to external track-and-trace platforms (e.g., TraceLink, rfxcel) or ERP modules (SAP ICH, Oracle Traceability), ensuring a single source of truth for downstream compliance reporting.

Automated logic handles exceptions and high-stakes events. If a scan fails or a lot is flagged for quarantine, agents route the physical item to a hold area and create a case in a Quality Management System (QMS). For a recall, the system instantly queries the graph to identify all affected serial numbers, their current locations, and downstream customers, generating pick lists and customer notifications automatically to contain the incident.

The workflow embeds controlled escalation paths. Exceptions (invalid scans, ambiguous labels) are routed to a supervisor dashboard for review. Major actions, like releasing a quarantined lot or executing a recall, require multi-factor approval via integrated systems like ServiceNow. All overrides and approvals are logged in the chain-of-custody graph, preserving accountability and meeting regulatory requirements for human oversight.

Deployment is phased, starting with a single receiving dock or high-value product line. The architecture uses containerized microservices for capture agents and the orchestration engine, allowing scaling without disrupting core WMS operations. A digital twin of the material flow can simulate label scanning rates and exception volumes to validate throughput before go-live, de-risking the integration with live automation equipment and legacy systems.

Every scan event—from receiving to picking to shipping—must create an immutable, timestamped record linked to the user, device, and location. The workflow architecture must integrate with systems like SAP ICH or Oracle Traceability to write these transactions directly, preventing gaps that could invalidate a recall or regulatory audit. This requires event-sourcing patterns and cryptographic hashing at key handoff points.

Automated scanning via fixed-mount or robot-mounted cameras is critical for throughput, but smudged, torn, or missing labels are inevitable. The workflow must include a confidence-scoring layer that routes low-confidence scans to a human-in-the-loop validation station without stopping the line. This requires integrating vision AI (e.g., Cognex, Keyence) with the WMS task engine to dynamically re-route physical items.

Lot data is not just for tracking; it drives quality holds, expiration management, and compliance reporting. The workflow must bi-directionally sync with ERP (e.g., SAP EWM, Oracle WMS) and QMS (e.g., Trackwise, Veeva) systems. For example, a scan that matches a lot on quality hold must automatically trigger a physical diversion to a quarantine location and a case in the QMS.

Regulated environments cannot have silent failures. The workflow needs a dedicated exception-manager agent that classifies issues (e.g., duplicate serial number, parent-child lot mismatch) and routes them via predefined rules. High-severity exceptions must escalate to supervisors via Slack, Teams, or SMS with full context, while low-severity issues queue for batch review. This layer is built using a rules engine or a framework like LangGraph for stateful routing.

The business value is proven during a recall. The architecture must support sub-second trace queries both upstream (where did this component come from?) and downstream (where did this finished lot ship?). This requires a graph database or optimized OLAP layer separate from the transactional WMS. The workflow should include automated agents that run periodic recall simulations to validate query performance and data integrity.

A "big bang" rollout is too risky. Implementation should follow a phased pilot: start with a single receiving dock, then a picking zone, then full facility. Each phase must have a parallel run where automated scans are compared against manual barcode scans to validate accuracy. The workflow's logic and rules must be managed under formal change control, with versioning and rollback capabilities built into the orchestration platform (e.g., using Kubernetes for agent deployment).