Optimizing Order Sourcing for Markdown Avoidance Through the Agentic Shift

In contemporary retail, a product’s “markdown” often signifies a mistake in its initial placement or distribution, rather than a deficiency in the product itself. Every markdown represents a loss of potential revenue. A persistent challenge for retailers is finding the right balance: effectively satisfying customer demand while simultaneously mitigating the accumulation of excess stock. Studies reveal that non-moving or dead stock carries a heavy financial burden. This is due to the costs associated with the tied-up capital in unsold goods, storage space, insurance, taxes, and depreciation. This often leads to a higher opportunity cost, as money spent on dead stock cannot be used for profitable, fast-moving items. Retailers often resort to markdowns and clearance to mitigate it.

A proactive and intelligent sourcing approach can help minimize markdowns, though. Consider a scenario where a winter coat sitting in a warm San Diego store will eventually be put on clearance, while the same coat in Chicago might be sold out. Traditional Order Management Systems (OMS) attempt to solve the order sourcing with static logic—”fulfill from the closest store”—but they lack the nuance to understand the future demand and the external factors.

Unlike traditional sourcing logic, which follows a static set of rules, AI Agents can reason, gather multi-modal context, and make autonomous decisions to protect the margin. Retailers can define markdown avoidance rules using a distributed logic executed by agents. The agents evaluate incoming orders (demands), external events, social trends, and fulfillment nodes based on their on-hand stock, current and historical fulfillment rate, and any planned incoming stock. Basically, this sets up a mechanism to derive the optimal node to fulfill an order by ranking locations based on how likely they are to sell that item quickly.

See also: How Real-Time Data Is Transforming Day-to-Day Retail Decisions

Traditional Sourcing vs. Dynamic Reality

Most OMS platforms provide static sourcing rules: fulfill from the closest store, or fulfill from the store with the most stock. While effective for basic operations, these static rules fail to account for multiple different factors that could impact a product’s sell-through velocity and the true cost of fulfillment. This often results in:

• Trapped Inventory: Products may be sitting in a slow-selling store and will eventually be put on clearance.
• Missed Sales: Fast-selling stores running out of stock while inventory is available elsewhere.
• Inefficient Logistics: Suboptimal routing leading to higher shipping costs or delays.

See also: Real-Time Visual Intelligence in Retail

The Agentic AI Solution for Dynamic, Data-Driven Prioritization

An agentic AI system transforms an order sourcing engine from a rigid rulebook into a dynamic, adaptive one. AI agents actively determine the optimal fulfillment choice by taking a comprehensive view of the supply chain and factoring in local demand signals.

Here is how this agentic architecture works in practice.

1. Data Gathering

When an order is placed, the OMS triggers a Sourcing Orchestrator Agent. This orchestrator dispatches sub-agents to gather intelligence on every potential fulfilling node (stores, warehouses, drop-ship vendors, or dark stores).

A. Pending Stock Check

Static systems only look at the current “On Hand” quantity. An agent looks at velocity and incoming flow. The following data is gathered for ranking:

• Inbound Manifests: The agent checks whether a replenishment is arriving at Store A in the next ‘x’ hours. If so, it might be better to fulfill from there rather than depleting the last unit at Store B.
• Inbound Returns: By analyzing return initiation data (RMAs generated online that are in transit but not yet received), the agent can “see” stock that may re-enter the system.
• Lost Inventory Check: The agent analyzes the RFID scan history. If an item hasn’t been scanned in ‘x’ days despite showing “in stock,” the agent flags it as “likely lost/stolen” and downgrades that store’s ranking to avoid a cancellation.

B. Fulfillment Rate Score

Avoiding markdowns is useless if the order is cancelled due to operational failure. A historical and real-time measure of how quickly and reliably a particular location fulfills orders for that specific SKU. A store with a high fulfillment rate is generally more efficient. The following factors are analyzed:

• Capacity Analysis: The agent queries the workforce management system. Is the store understaffed today? If Store A has call-outs and a backlog of BOPIS (Buy Online, Pick Up In Store) orders, its fulfillment capacity is effectively zero.
• Advance Notification Window: If an order is dropped for fulfillment now, can the store pick, pack, and ship it on time?
• Historical Trend: The agent reviews historical data. Does this store frequently miss the carrier pickup window? If the customer paid for expedited shipping, this store is deprioritized regardless of inventory levels.

C. Local Event & Demand Sensing

Agentic AI is highly effective here, bridging external unstructured data with internal inventory systems. AI agents leverage information from event calendars, news feeds, and social media to pinpoint local happenings that could either increase or decrease demand. Examples include major sporting events boosting team merchandise sales, festivals drawing more visitors, or local disruptions such as road closures impacting foot traffic. The following data is evaluated:

• Event Correlation: The agent integrates with local event APIs (e.g., Ticketmaster, Eventbrite). For example, if a music concert is happening at a venue, the agent protects the stock of related or associated merchandise at nearby stores. It deprioritizes stores to fulfill online orders for customers in other states, preserving that stock for high-margin, in-store traffic.
• Micro-Trend Analysis: The agent monitors local social media trends. For example, if a specific sneaker is trending on social media among users in Los Angeles, the agent “ring-fences” LA inventory for local full-price sales.

D. Weather Factors

Weather drives immediate need. Agents use this to predict “distressed inventory.” Current and forecasted weather conditions directly impact demand and logistics. Heavy rain might suppress in-store shopping, making local delivery a priority. Similarly, a heatwave could spike demand for summer apparel.

Let’s understand it with the following examples. Imagine a scenario where a city may be expecting a week of storms. Hence, the agent will prioritize preserving the rain gear in the local stores. It will route online orders to be fulfilled from a store where it is dry, and the item is likely to sit stagnant. Conversely, if a cold front hits the city, winter coats there suddenly have a high full-price sell-through potential. The agent stops using local stores as fulfillment hubs for online orders, forcing the OMS to source from a warmer region where the coats are “dead stock.”

E. Supply Chain Continuity

Agents monitor inbound (procurement) supply chain data, such as supplier delays, port congestion, shipping lane disruptions, or even warehouse labour shortages, to understand potential future impacts on stock replenishment for each location.

• Logistics Monitoring: The agent detects that a container ship carrying replenishment stock for the East Coast is delayed by 2 weeks. It immediately locks down current inventory on the East Coast, forcing fulfillment from West Coast hubs where stock is plentiful. This prevents the East Coast stores from running empty (stockouts) while the West Coast sits heavy (markdown risk).

2. Node Ranking

Using a vast amount of real-time data, agents create a sophisticated ranking for each potential fulfillment store. This ranking goes beyond simple metrics like distance or inventory. The primary goal of this function is to achieve two key priorities: minimizing markdowns and ensuring fast customer delivery.

The agents would weigh factors such as:

• Idle stock: Stores holding high quantities of end-of-season or slow-moving items for a specific SKU would be prioritized to clear that stock before it becomes a markdown liability.
• Demand Velocity: If local events or weather suggest a surge in demand (or lack thereof) for a particular SKU, agents can adjust priority. A store predicted to sell out quickly might be given a lower fulfillment priority compared to another store that has excess stock and is at markdown risk.
• Cost Analysis: While markdown avoidance is primary, agents can also factor in shipping costs and delivery speed. This ensures a balanced decision. For instance, shipping from a slightly further store might be justified if it prevents a significant markdown.
• Fulfillment Rate: Stores with proven high fulfillment rates reduce the risk of delays and customer dissatisfaction.

Once the sub-agents return their data, the Orchestrator Agent compiles a Fulfillment Priority Score (FPS) for each store. This score can then be used to rank stores for fulfillment priority.

The agent constructs a ranking as follows:

1. Highest Priority: Stores where the item is “Dead Stock” (No local sales in ‘x’ days + Weather mismatch + No local events). Ship from here immediately to clear space.
2. Medium Priority: Stores with excess inventory level – High inventory depth vs. average sell-through.
3. Low Priority: Stores with “High Velocity” (Low weeks-of-supply + Incoming local event + Perfect weather match). Do not fulfill online orders from here; save for walk-in customers.

3. Dynamic Routing

Based on this constantly updated, intelligent ranking, the OMS, guided by the agentic AI, makes the optimal sourcing decision for each order. This isn’t a static decision; if conditions change (for example, a sudden weather alert or a new local event), the agent can re-evaluate and reroute orders that haven’t been picked yet.

Benefits Beyond Markdown Avoidance

Implementing agentic AI for order sourcing yields a multitude of advantages:

• Significant Reduction in Markdowns: This is the primary goal, directly impacting profitability.
• Optimized Inventory Utilization: Every unit of stock is placed in the best position to sell at full price.
• Improved Customer Satisfaction: Faster, more reliable fulfillment and product availability.
• Enhanced Operational Efficiency: Reduced manual intervention in complex sourcing decisions.
• Proactive Problem Solving: Agents can identify potential stockouts or overstocks before they become critical.
• Greater Resilience: The system adapts dynamically to unforeseen disruptions like extreme weather or supply chain shocks.

The adoption of agentic AI represents a fundamental transformation for order management systems. It transforms them from reactive engines to proactive, intelligent strategists. Retailers are beginning to leverage online demand to efficiently clear out slow-moving inventory that is located in suboptimal locations. This technology is key to more than just fulfilling orders; it’s about orchestrating a truly seamless, profitable, and adaptive supply chain. By intelligently sourcing each order, we maximize full-price sales and enhance customer satisfaction.