AI-Driven Workflow for Perishable Goods and FIFO/LIFO Management

This workflow automates the critical operational bottleneck of manually tracking and enforcing First-In-First-Out (FIFO) or Last-In-First-Out (LIFO) picking logic for perishable inventory. It eliminates waste from expired goods and prevents compliance violations by integrating lot-tracking systems (e.g., SAP, Oracle) with warehouse execution. The architecture ingests real-time data from WMS and ERP to dynamically reorder pick lists and replenishment tasks, ensuring the oldest or most urgent inventory is always prioritized, directly protecting margin and reducing write-offs.

Implementation requires a central orchestrator, like LangGraph, to manage state between the WMS, a dedicated lot database, and the warehouse control system (WCS). The system must handle exceptions—such as mis-scanned lots or damaged goods—by routing them to a human review station with full audit trails. Rollout sequencing typically starts with high-velocity SKUs, integrating vision systems for automated lot validation at pick and pack stations to ensure end-to-chain traceability and defensible compliance reporting.

This workflow automates the critical bottleneck of manual lot tracking and date-checking, directly attacking waste and compliance risk. It integrates with your WMS and lot-tracking systems (e.g., SAP EWM, Oracle) to continuously evaluate inventory against expiration thresholds. The system's value comes from eliminating human error in picking sequence, preventing costly write-offs of expired goods, and providing an auditable trail for regulators. Implementation starts with ingressing real-time inventory positions and lot attributes, then applying configurable business rules for FIFO, LIFO, or FEFO logic.

The architecture is built on an event-driven orchestrator, likely using LangGraph or a custom microservices layer, to process state changes and dispatch commands. Controls are essential: low-confidence matches or near-expiry alerts are routed to a human review dashboard in systems like ServiceNow. Monitoring tracks key metrics like 'days-to-expiry' trends and prevented waste value. Rollout requires phased integration, starting with high-value SKUs, and digital twin simulation to validate pick-path logic before live deployment with warehouse robotics.

This workflow automates the critical bottleneck of manual lot tracking and date-checking, directly attacking the 5-10% of revenue lost to perishable waste. By integrating with your WMS (e.g., SAP EWM, Blue Yonder) and lot-tracking systems, it continuously scans inventory positions, applies FIFO/LIFO rules, and dynamically reprioritizes pick paths and replenishment tasks. The operational upside comes from eliminating expired shipments, reducing manual audit labor, and preventing costly regulatory fines through automated, audit-ready compliance logging.

Implementation is phased, starting with a rules engine and API layer to your WMS, using a framework like LangGraph for agentic orchestration. Phase one establishes lot-aware picking, while phase two adds predictive alerts for soon-to-expire inventory, triggering markdown or donation workflows. Governance is built-in: low-confidence picks are routed for human review via a tablet interface, and all decisions are logged with the rationale for full traceability during audits. The system requires clean master data but delivers ROI within one peak season through waste reduction alone.

This workflow automates the critical bottleneck of manual lot tracking and date-checking, directly reducing spoilage costs and compliance risk. It integrates with your WMS and ERP (like SAP or Oracle) to ingest real-time inventory positions, applying FIFO/LIFO rules via an agentic orchestrator. The system's value comes from eliminating manual errors in picking sequence, preventing costly recalls, and recovering margin by prioritizing at-risk stock for sale or donation before it expires, typically delivering a full ROI within 12-18 months through waste reduction alone.

Implementation requires a LangGraph or custom agent framework to manage state across the picking, replenishment, and alerting logic. The architecture must include real-time integration with warehouse execution systems and robotics control for physical enforcement. Critical controls include human-in-the-loop approval gates for disposition decisions, immutable audit trails for regulatory reporting, and digital twin simulation to validate rule changes before deployment, ensuring the system adapts to seasonal shifts without disrupting high-velocity operations.

This workflow automates the critical but repetitive task of enforcing First-In-First-Out (FIFO) or Last-In-First-Out (LIFO) picking logic for perishable inventory. By integrating directly with lot-tracking systems (e.g., SAP, Oracle) and Warehouse Management Systems (WMS), it eliminates manual date-checking, prevents costly spoilage, and ensures regulatory traceability. The operational upside comes from reducing write-offs by 15-30%, improving warehouse throughput by ensuring pickers are always directed to the correct lot, and automating compliance reporting for audits.

Implementation requires building an agentic orchestration layer, typically with LangGraph or a custom microservice, that sits between the WMS and warehouse control systems. It ingests real-time pick tasks, queries the lot master database for expiration dates and physical locations, and dynamically routes robots or staff to the correct pallet. Critical controls include configurable expiry thresholds for alerting, mandatory human review for near-expired goods, and immutable audit logs for every lot movement to satisfy FDA or EU regulatory requirements.