Multi-Warehouse Order Routing

Organizations operating distributed fulfillment networks face a fundamental allocation challenge: determining which warehouse, distribution center, or store should fulfill each incoming order. Static routing rules, often embedded in legacy enterprise resource planning systems, assign orders based on fixed logic such as default warehouse proximity or predetermined regional assignments. These rigid approaches fail to account for real-time inventory fluctuations, carrier rate changes, or shifting demand patterns, resulting in suboptimal fulfillment decisions that compound across thousands of daily orders. According to a 2023 BeProfit analysis published by Statista, shipping services accounted for 88% of allocated e-commerce fulfillment costs in the United States, making routing efficiency a direct lever on profitability.

The financial stakes are considerable. McKinsey research estimated that omnichannel order fulfillment costs represent roughly 10% to 20% of sales in omnichannel retail, and last-mile delivery now accounts for 53% of total shipping costs according to a 2025 Pitney Bowes parcel industry report. Split shipments, which occur when a single order ships from multiple locations, increase per-order shipping costs by 25% to 35% according to a 2025 FreightAmigo analysis. Industry data indicates that split-shipment rates commonly range between 10% and 40% of multi-item orders, driven by SKU dispersion across fulfillment nodes and safety-stock policies.

The complexity intensifies as retailers expand fulfillment options. Major omnichannel retailers now fulfill over half of online orders from store locations, as reported by Walmart in 2024, while a large general merchandise retailer reported fulfilling over 80% of online orders from stores as of late 2023. This proliferation of fulfillment nodes, spanning distribution centers, stores, dark stores, micro-fulfillment centers, and third-party warehouses, makes manual or rules-based routing increasingly untenable at scale.

AI-driven multi-warehouse order routing employs multi-constraint optimization models that simultaneously evaluate inventory availability, geographic proximity, shipping costs, carrier capacity, delivery service-level agreements, and fulfillment node workload to select the optimal source for each order. Unlike static rules engines that evaluate a single variable such as nearest warehouse, machine learning models weigh dozens of parameters in real time, including zone-based carrier rates, node-level capacity constraints, and historical fulfillment performance. Manhattan Associates, for example, describes its adaptive network fulfillment technology as using machine learning to dynamically optimize fulfillment decisions by evaluating parameters across distribution centers, transportation options, stores, and customer locations.

The technical architecture typically integrates three core components. First, a real-time inventory visibility layer aggregates stock positions across all nodes, including on-hand, in-transit, and allocated inventory, to prevent routing decisions based on stale data. Second, a constraint-optimization engine applies algorithms, often combining linear programming with reinforcement learning, to evaluate fulfillment scenarios and select the lowest-cost path that meets the delivery promise. Third, a continuous learning module analyzes actual fulfillment outcomes, carrier performance data, and cost variances to refine routing logic over time. These components sit within or alongside distributed order management platforms, which Gartner defined in its 2024 Market Guide as software that orchestrates and optimizes the order fulfillment process.

Implementation requires integration with warehouse management systems, transportation management systems, carrier rate APIs, and e-commerce platforms. The 2025 Gartner Market Guide for Distributed Order Management Systems noted that retailers and B2B companies face increased demands for inventory visibility and segmentation across multiple sales channels and marketplaces. A 2025 Mecalux-MIT survey of more than 2,000 warehouse leaders found that data quality, legacy system integration, and scarce technical talent remain the primary barriers to full AI value realization in warehouse and fulfillment operations.

Organizations should set realistic expectations regarding these systems. AI-based routing does not eliminate the need for sound network design or adequate inventory positioning. Models require clean, consistent data feeds across all nodes, and performance degrades when inventory accuracy falls below acceptable thresholds. Additionally, multi-objective optimization introduces trade-offs: minimizing shipping cost may conflict with minimizing delivery time or balancing workload across nodes, requiring organizations to define clear priority hierarchies.

A large U.S. general merchandise retailer has become one of the most visible adopters of store-based intelligent order routing, fulfilling over 80% of online orders from store inventory as of late 2023, according to Digital Commerce 360 reporting. The retailer invested in training thousands of store employees as omnichannel fulfillment associates and hired over 100,000 seasonal workers in 2023, many allocated to store fulfillment, to meet peak demand. The approach leverages AI-powered order management to route each order to the optimal store or distribution center based on inventory levels, distance to the customer, and delivery capacity. The result has been faster delivery times and improved cost efficiency by using existing store infrastructure rather than relying solely on centralized distribution centers.

A large European retailer with more than 25,000 products and over 20,000 employees deployed an AI-powered logistics optimization platform to improve distribution center shipment operations. According to a ThroughPut AI case study published in 2025, the retailer reduced logistics costs by 33%, approximately 2 million euros per year, and reduced SKU volume by 20% and transportation costs by 33% through AI-driven demand sensing and shipment optimization. The implementation delivered measurable results within 90 days of deployment. Separately, a global footwear and accessories retailer reported that after deploying a distributed order management system, store networks were able to fulfill Black Friday order volumes up to seven times higher than the prior week in equal or even half the fulfillment time, according to statements from the retailer's vice president of post-purchase operations.

The distributed order management market has matured rapidly, with Gartner publishing its 2025 Market Guide for Distributed Order Management Systems in June 2025 and Forrester naming leaders in its Q1 2025 Order Management Systems Wave. The market segments into three tiers: enterprise-grade platforms offering AI-driven optimization and deep supply chain integration, mid-market solutions focused on rules-based routing with emerging AI capabilities, and composable commerce platforms that embed order orchestration within broader commerce suites. B2B adoption is accelerating alongside B2C, as Gartner noted increased interest from B2B organizations whose digital commerce orders increasingly resemble B2C fulfillment patterns.

Selection criteria should include the depth of real-time inventory visibility across node types, the sophistication of optimization algorithms (rules-based versus machine learning), integration flexibility with existing ERP, WMS, and TMS systems, support for multiple fulfillment models including ship-from-store and drop-ship, and the ability to balance competing objectives such as cost, speed, and sustainability. Organizations should also evaluate vendor architecture, favoring cloud-native, microservices-based platforms that support composable deployment and continuous updates without system-wide disruption.

• Manhattan Associates -- cloud-native order management and fulfillment optimization platform with machine learning-driven adaptive network fulfillment, real-time inventory visibility, and unified omnichannel orchestration across DCs, stores, and drop-ship partners
• Blue Yonder -- end-to-end supply chain and order management suite with distributed order orchestration, inventory optimization, and AI-driven commit and fulfillment services
• Fluent Commerce -- cloud-native distributed order management platform with flexible rules-based fulfillment logic, real-time inventory hub, and configurable store fulfillment workflows
• IBM Sterling Order Management -- enterprise order orchestration platform with inventory visibility, intelligent promising, and multi-node fulfillment optimization capabilities
• Kibo Commerce -- distributed order management system with AI-powered routing agents, rules-based fulfillment optimization, and MACH-architecture for composable deployment
• Deck Commerce -- SaaS order management system for direct-to-consumer and omnichannel retailers with automated order routing, store fulfillment, and returns management
• Fabric (formerly CommonSense Robotics) -- order management platform targeting mid-market retailers with intelligent routing, inventory availability, and fulfillment orchestration