Multi-Agent System for Dead Stock Identification and Liquidation

This foundational agent continuously analyzes ERP and WMS data to calculate SKU-level turnover rates, days of supply, and shelf-life status against configurable business rules. It flags items as 'at-risk' based on velocity thresholds and aging criteria, creating the initial dead stock candidate list. The agent integrates with systems like SAP or Oracle to pull real-time inventory positions and sales history.

This agent enriches the candidate list with external market data, including competitor pricing, secondary market demand (e.g., B2B marketplaces), and regional sales trends. It scores each SKU on its liquidation potential across different channels (e.g., direct discount, bundling, B2B bulk sale, donation) to recommend the highest-value recovery path. The architecture includes APIs to pricing intelligence and e-commerce platforms.

The core orchestrator agent, built on a framework like LangGraph, receives scored recommendations and executes the liquidation workflow. It triggers specific actions: generating markdown prices in the ERP, creating promotional bundles in the e-commerce platform (e.g., Shopify), issuing RFQs to B2B liquidation partners, or scheduling goods movement for donation. It manages the state and data flow between all specialized agents and systems.

Not all actions are fully autonomous. This control layer routes high-value or non-standard liquidation proposals (e.g., large bulk sales, strategic channel shifts) to human stakeholders in finance, sales, or category management for approval via Slack, Teams, or a dedicated dashboard. It manages escalation paths, collects rationale, and logs all decisions for auditability and policy compliance.

This agent closes the feedback loop by tracking the outcomes of each liquidation action—revenue recovered, cost of sale, warehouse space freed—and comparing them to the initial forecast. It uses this data to retune the scoring models and business rules, creating a self-improving system. It also updates financial records and generates reports for working capital analysis.

A production build requires a microservices or agent-based architecture hosted on cloud infrastructure (AWS, GCP). Key integrations include: ERP Connectors (SAP S/4HANA, Oracle NetSuite) for inventory and financial data; E-commerce APIs (Salesforce Commerce Cloud, Shopify) for price updates; CRM/Marketing Platforms for campaign execution; and Data Warehouses for historical analysis. Deployment involves a phased pilot on a single category or warehouse before full rollout.

The primary source of truth for inventory data. The workflow ingests SKU-level details on on-hand quantities, cost, last receipt date, and storage location via direct API or nightly batch. This data layer is essential for calculating days of supply, turnover rates, and carrying cost exposure. Integration with systems like SAP S/4HANA, Oracle NetSuite, or Microsoft Dynamics is non-negotiable for accurate financial modeling and order creation.

Provides the demand signal needed to classify stock as 'dead'. Agents consume historical sales velocity, regional sell-through, and pricing history from platforms like Shopify, Salesforce Commerce Cloud, or in-store POS systems. This data feeds the initial identification model, distinguishing between seasonal lulls and genuine obsolescence. Real-time APIs enable the system to monitor the impact of liquidation promotions instantly.

Critical for understanding physical constraints and fulfillment logic. The workflow queries the WMS (e.g., Manhattan, Blue Yonder) for lot/batch details, expiration dates (for perishables), pick location efficiency, and available warehouse space. This informs liquidation prioritization—older stock in prime picking locations gets flagged first. Post-decision, the system can trigger internal transfer orders or pick waves via WMS APIs to consolidate dead stock for liquidation channels.

The engine for executing liquidation strategies. Once dead stock is identified, agents call pricing engines and promotion platforms (like Vistaar, PROS) to calculate optimal markdown percentages, bundle pricing, or flash sale triggers. The workflow must adhere to pre-configured margin floors and brand price-image rules. Approved price changes are then pushed back to the POS, e-commerce, and marketplaces (e.g., Amazon, eBay) via their respective APIs.

Direct integration points for moving identified stock. The system connects to B2B liquidation marketplaces (e.g., B-Stock, Liquidity Services), donation partners, and third-party logistics (3PL) providers for refurbishment or recycling. Workflow agents use these APIs to list pallet quantities, negotiate bulk pricing, generate shipping labels, and update inventory levels upon sale. This closes the loop, converting dead assets into recovered capital.

The control plane for consistent decision-making. This isn't a single system but a logical layer that provides agents with approved product hierarchies, brand/category rules, compliance flags (e.g., regulated items), and financial GL codes. It ensures liquidation actions for a high-end fashion SKU differ from those for a commodity hardware item. This layer is often built atop a PIM or MDM system and is essential for auditability and preventing brand damage.