Allocation & Replenishment
Flow the right inventory to the right place under real-world constraints
Allocation and replenishment decisions determine whether inventory investments translate into on-shelf availability or excess stock. As retailers adopt omnichannel fulfillment and face increasing supply chain volatility, traditional rule-based replenishment approaches struggle to keep up. Inventory must be positioned not just where demand exists, but where it can be fulfilled profitably and on time.
o9 Allocation & Replenishment enables retailers to plan and execute inventory flows using a constraint-aware, network-wide perspective. Built on a digital twin of the supply network, the solution aligns demand, inventory policies, and logistics capacity across multiple planning horizons—long-term, mid-term, and daily execution.
This approach transforms replenishment from reactive order generation into proactive flow orchestration, improving service levels while controlling working capital.
When rules meet real-world constraints
Traditional replenishment systems assume infinite capacity. In reality, labor, transport, storage, and lead times all impose hard limits on what can be executed. When these constraints are ignored, plans break down downstream—resulting in split shipments, missed promotions, and service failures.
Omnichannel fulfillment further complicates allocation decisions. Stores now serve customers directly, act as fulfillment nodes, and compete for limited inventory. Without visibility into demand supportability, retailers struggle to prioritize inventory effectively.
From rule-based replenishment to flow orchestration
Leading retailers are adopting flow-based planning approaches that align inventory movement with capacity and cost. Instead of asking “what should be replenished,” they ask “what can be fulfilled, when, and at what cost.”
o9 enables this shift by integrating demand sensing, inventory optimization, and constrained flow planning within a single execution framework.
What Makes o9 Different
Constraint-Aware Allocation
Plans account for labor, transport, and storage constraints.
Multi-Horizon Flow Planning
Inventory is planned across strategic, tactical, and execution horizons.
Demand Supportability Analysis
Teams understand which demand can be fulfilled on time—and which cannot.
Automated Execution
Planning decisions flow directly into execution systems.
Industries Supported
Powered by the o9 Digital Brain
Allocation & Replenishment operates on a digital twin of the supply network modeled within the o9 Digital Brain. This enables real-time propagation of disruptions and capacity changes across plans.
Modular by design, enterprise by default
The o9 Size & Pack Optimization solution is built on a high-performance, attribute-driven data model that translates localized demand into executable size and pack directives. Rather than treating sizing as a static input, the architecture continuously refines size intelligence based on real sales behavior and execution outcomes.
Core Building Blocks
Network-Wide Inventory Visibility
This capability provides a unified view of inventory across all nodes, including stores, distribution centers, in-transit inventory, and open orders. Visibility into on-hand, on-order, and in-transit inventory enables accurate replenishment and allocation decisions based on real availability rather than assumptions.Replenishment Planning Engine
The replenishment engine calculates store and DC replenishment needs based on forecasts, inventory policies, and safety stock targets. It determines when and how much to replenish to maintain desired service levels while controlling inventory investment.Allocation Rules and Prioritization Logic
Allocation logic distributes inventory across locations based on configurable business rules such as store priority, channel profitability, launch timing, or promotional importance. This ensures scarce inventory is directed to the most strategic destinations.
Order Generation and Execution Integration
Planned replenishment and allocation decisions are translated directly into executable stock transfer orders and purchase orders, reducing manual effort and ensuring consistency between planning and execution systems.
Advanced Building Blocks
Constrained Supply Chain Solver
This solver accounts for real-world constraints such as labor capacity, transportation limits, storage availability, and lead times. Rather than assuming infinite capacity, it identifies bottlenecks and prescribes feasible resolution scenarios.Multi-Horizon Flow Planning
Inventory flows are planned across multiple horizons—long-range (90 days), mid-term (21 days), and daily execution. This aligns replenishment decisions with logistics capacity and operational readiness before issues materialize.Multi-Echelon Inventory Optimization (MEIO)
MEIO optimizes safety stock levels simultaneously across multiple tiers of the network, such as DCs and stores. This balances service levels against working capital by positioning inventory where it provides the greatest value.
Demand Supportability Analysis
This capability evaluates which demand can be fulfilled on time given current inventory, capacity, and lead times. Planners gain early visibility into at-risk demand and can proactively prioritize, reallocate, or expedite inventory.
A digital operating model for VUCA conditions
APEX is o9’s AI-powered operating model for enterprises navigating volatility, uncertainty, complexity, and ambiguity (VUCA). It enables organizations to plan, execute, and learn as one connected system.
The o9 Digital Brain powers APEX by connecting enterprise data, knowledge, and decisions through a single intelligent model.
Collaborative Demand Planning is one of the building blocks of the Digital Brain. It contributes domain-specific capabilities into the enterprise-wide model that enables APEX from the ground up—linking this solution to decisions across the entire value chain.
→ Learn how the APEX Operating Model works
Where AI drives real decisions
AI enhances Allocation & Replenishment by shifting inventory flow decisions from rule-based execution to constraint-aware optimization.
Machine learning models improve short-term demand sensing using POS trends, promotions, weather, and local events.
Prescriptive analytics recommend allocation and replenishment actions while quantifying service and cost trade-offs.
Generative AI enables natural-language exploration of flow decisions, while agentic AI monitors execution risks and recommends corrective actions.
→ Learn more about o9 AI innovations
What our customers say
"We made the conscious decision with o9 to bring a quicker ROI by integrating with our legacy SAP. [...] When the full ERP transformation happens, we’re ahead of the game."
Paul Tips
Product Owner at Canyon Bicycles
"What's really succeeding with us is the idea of the connection to the data and a best-in-class UX/UI, so the people that use the business can really make an impact."
David Almeida
Chief Strategy & Technology Officer at Anheuser-Busch InBev
"With o9 AI/ML-based forecasting in place, we’re already seeing improved forecast accuracy, stronger cross-functional collaboration, and faster, more informed decision-making—all within a centralized platform."
Gaby Gutierrez
VP of Global Supply Chain Planning at Amway
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Frequently Asked Questions (FAQ)
Replenishment planning ensures timely restocking by using demand forecasts and inventory data to prevent stockouts, reduce excess, and optimize inventory flow.