AI Workflow for Slow-Moving Stock Automated Clearance Pricing

Slow-moving stock ties up capital in storage fees, insurance, and opportunity cost. An automated clearance workflow identifies these items using turnover velocity and days-on-hand metrics, then systematically liquidates them. This converts dormant assets into cash, which can be redeployed into faster-moving inventory or other business investments, directly improving the balance sheet.

Manual clearance often involves a single, deep discount that leaves money on the table. An AI-driven workflow applies a time-based discount schedule, starting with a modest markdown and escalating only if the item doesn't sell within a defined window. This demand-testing approach, informed by similar product elasticity, typically recovers 20-40% more revenue than a one-size-fits-all fire sale.

Merchandising and inventory teams spend days each quarter manually running reports, setting discount levels, and updating prices across channels. This workflow automates the entire lifecycle: classification, discount scheduling, price calculation, and execution via ERP (SAP, Oracle) and e-commerce platform APIs (Shopify, Magento). It redirects skilled labor to strategic planning, reducing operational overhead by 60-80%.

Stagnant inventory occupies prime picking locations, slowing fulfillment and increasing operational complexity. By automating clearance, you create a predictable flow of stock designated for liquidation. This frees up valuable warehouse cubic footage for faster-moving goods, improving pick rates and reducing the need for costly warehouse expansion. The system can even trigger transfer orders to outlet or discount channels.

Without proactive management, slow-moving stock becomes obsolete, resulting in a 100% loss. The automated workflow acts as an early-warning and intervention system. It continuously monitors age and seasonality, initiating clearance actions well before items become unsellable. This proactive governance layer turns potential total losses into partial recoveries, directly protecting gross margin and reducing P&L volatility.

The clearance process generates high-quality data on what doesn't sell and at what price it finally clears. This workflow feeds performance analytics—like final liquidation price, time to sell, and channel effectiveness—back into procurement and merchandising systems. This creates a closed-loop learning system that improves future buying decisions, assortment planning, and initial pricing strategies, reducing the future volume of slow-moving stock.

This foundational agent continuously monitors ERP/WMS data (e.g., SAP, Oracle) to score SKUs based on velocity, days of supply, and seasonality. It classifies inventory into lifecycle stages (e.g., 'healthy', 'at-risk', 'slow-moving') using configurable business rules, triggering the clearance workflow only for qualified stock. It integrates with financial systems to understand carrying cost, creating the business case for each automated markdown.

This agent designs the markdown campaign. For each SKU cohort, it calculates an optimal discount curve—a series of progressively deeper price cuts scheduled over time—based on historical elasticity, competitor clearance prices, and target sell-through dates. It simulates outcomes to balance liquidation speed against margin recovery, outputting a time-bound action plan to the execution layer.

A critical control layer that validates every proposed price change against live product cost (from ERP) and minimum margin thresholds before execution. It acts as a pre-flight check, blocking any action that would violate configured business rules. All decisions and rationales are logged to an immutable audit trail for finance and compliance review, ensuring automated actions remain profitable and defensible.

This execution agent receives approved price changes and orchestrates their publication across all relevant sales channels. It manages API integrations with e-commerce platforms (Shopify, Magento), marketplaces (Amazon, Walmart), and in-store systems like Electronic Shelf Labels (ESL). It handles rate limits, error recovery, and rollback procedures, ensuring price synchronization is reliable and consistent across the customer journey.

This observability agent tracks the impact of each markdown campaign in real-time, monitoring sales velocity, margin attainment, and inventory depletion. It compares actual performance against forecasts, identifying underperforming SKUs for manual escalation or curve adjustment. Its analysis feeds back into the classifier and optimizer agents, creating a closed-loop learning system that improves future clearance decisions.

Not all decisions can be fully automated. This component provides structured escalation paths for exceptions—such as high-value items, proposed discounts below a severe threshold, or system confidence scores below a set level. It routes these cases to a configured queue in tools like ServiceNow or Jira for merchant or finance review, blending autonomous efficiency with necessary human oversight for complex judgments.