Automation Workflow for Perishable Goods Shelf-Life Optimization

This workflow automates the critical bottleneck of manually tracking expiration dates and prioritizing stock rotation. It directly protects margin by reducing spoilage write-offs and ensures regulatory compliance for FEFO (First-Expiry-First-Out). The system integrates with Warehouse Management Systems (WMS) like SAP EWM or Oracle WMS and demand planning platforms, using agents to continuously analyze lot data, storage conditions, and forward demand signals to calculate optimal picking sequences and markdown recommendations before value erodes.

Implementation requires building an orchestration layer, likely with LangGraph or a custom microservice, that brokers data between ERP, WMS, and forecasting systems. Key controls include human-in-the-loop approval gates for significant markdowns or donations, confidence scoring on demand forecasts, and real-time observability into waste metrics. Exception routing handles data mismatches, such as missing lot dates, while phased rollout can start with high-value SKUs before scaling across the perishable catalog.

This workflow directly attacks the margin erosion from expired inventory by automating the First-Expiry-First-Out (FEFO) principle. It replaces manual lot tracking and reactive markdowns with a continuous orchestration layer that connects warehouse execution systems (WES/WMS), demand forecasts, and sales channels. The operational upside comes from reducing waste by 15-30%, improving labor efficiency in picking, and capturing more value through optimized markdown timing and donation routing, all while ensuring regulatory compliance for lot traceability.

Implementation integrates via APIs with systems like SAP WM, Oracle WMS, or Blue Yonder. The orchestrator, built on frameworks like LangGraph, triggers the demand agent to prioritize at-risk lots, the routing agent to direct pickers via RF guns or lights, and the markdown agent to push promotions. Critical controls include mandatory human review for quality exceptions, digital twin simulation for policy changes, and immutable audit logs for lot disposition to satisfy FDA 21 CFR Part 11 or equivalent pharmaceutical traceability mandates.

Phase 1 establishes the data foundation. We integrate with your Warehouse Management System (WMS) and ERP to ingest real-time lot-level data: expiration dates, storage locations, and current inventory. Concurrently, we connect demand signals from your Order Management System (OMS) and forecasting engines. This creates a single source of truth, enabling the initial calculation of spoilage risk and priority scores for each lot, providing immediate visibility into waste exposure.

Phase 2 deploys the autonomous orchestration layer. The Priority Scoring Engine triggers actions based on configurable business rules. For high-risk lots, it directly instructs the WMS to create FEFO-compliant picking waves. For medium-risk, it routes markdown recommendations to pricing teams with calculated ROI. For non-sellable inventory, it initiates compliance-ready donation workflows. All actions flow through a human-in-the-loop approval queue for oversight before execution, ensuring control and auditability.

Effective governance begins with a centralized control plane that logs every agent decision—lot selection, markdown percentage, routing change—against the originating data signal and business rule. This audit trail is essential for regulatory compliance (FDA, FSMA) and internal quality audits. Approval gates are configured at the workflow orchestrator level, requiring human sign-off for high-value disposition actions or when confidence scores from vision or condition-monitoring agents fall below a defined threshold, ensuring safety and financial oversight are never fully automated.

Rollout should be phased, starting with a single DC or product category (e.g., dry goods) to validate data integration from the WMS (like Blue Yonder or SAP EWM) and the accuracy of the condition-monitoring agents. Phase two introduces markdown logic, initially in advisory mode, before enabling autonomous pricing updates in the ERP. The final phase expands to all perishable SKUs and integrates donation partner APIs. Each phase requires updated SOPs, targeted user training, and defined rollback procedures to manage risk without disrupting core warehouse operations.

For perishable goods, waste is a direct margin leak. A custom shelf-life optimization workflow automates the complex, daily task of reconciling lot-level expiration dates with dynamic demand forecasts and storage conditions. By integrating with WMS (like Blue Yonder or Manhattan) and ERP systems, it prioritizes picking and recommends markdowns or donations, converting spoilage risk into recovered revenue. The operational upside comes from labor savings in manual inventory checks and a measurable reduction in write-offs, often 15-30% in high-volume perishable operations.

Implementation requires a central orchestrator, like LangGraph, to manage state between your WMS, demand planning, and markdown systems. Data quality is critical; the workflow must handle missing lot dates or incorrect storage temps by routing exceptions to human review. Rollout starts with a single DC, using the observability dashboard to tune priority logic before scaling. Governance includes approval gates for donation decisions and scheduled model retraining using new spoilage patterns to maintain accuracy.