5
Up to 5% More Revenue
Retailers who move from static allocation rules to a dynamic, demand-driven allocation strategy add up to 5% in revenue and cut out-of-stock items by as much as 50%, according to industry research.
ActivePlanning
Allocation Planning That Finds the Right Fit Across Your Supply Chain
Built to adapt and grow with your business.
Manhattan's Transportation Management System (TMS) is cloud-native software that helps shippers plan, optimize, and execute freight movement — managing carriers, rates, routes, and loads across the entire transportation network in real time.
25
Fewer Days of Inventory, at Higher Service
Supply chain networks agile enough to reallocate inventory in real time carry 23 fewer days of it on average, while running service levels 7% higher than slower-moving peers.
72
Report Lower Operating Costs
72% of retailers using AI-driven allocation and replenishment report lower operating costs, and 69% report higher revenue.
1
Synergy Between Allocation and Order Management
Coupling allocation with Order Management provides retailers with real-time insights into inventory needs across all channels.
Allocation
Every Unit on the Shelf Where It Sells
A store sells out of a top size while the same item sits idle two states over, and a planner without full network visibility ends up with stockouts on one shelf and overstocking on another.
See Every Location at Once
Manhattan’s allocation software brings real-time data from stores, distribution centers, and online channels into one view, so a planner sees the location running short and the one with extra stock side by side.
Move Stock to Where It Will Sell
Store clustering and size-curve analysis match each unit to the location most likely to sell it, building the allocation from demand patterns, historical sales data, and market trends.
Keep Replenishment and Allocation on the Same Plan
Reorder points, lead times, and safety stock targets feed the same plan that decides where new inventory goes next. When supply chain disruptions hit, such as a delayed shipment, the plan picks them up early and adjusts demand and supply planning automatically.
The Competitive Edge of Unified Allocation
Manhattan's SCP offers a significant competitive advantage by fully unifying allocation with demand forecasting and replenishment. This integration delivers real-time responsiveness, omnichannel precision, and data-driven intelligence. Allocation decisions adjust dynamically based on the latest data, helping retailers align inventory across channels, maximize sales, minimize waste, and improve customer satisfaction.
Resources and Downloads
Features
Size, Store & Prepack Optimization
Get the right product to the right location, ready for how each customer wants to buy it: in-store, online, or curbside.
Size-optimization algorithms weigh local demand so a store gets the size curve its own customers buy.
Store clustering uses machine learning to group locations by how their customers shop, down to style and color, so two stores in the same region can get different allocations when their demand differs.
Prepack selection picks the best prepacks for each store based on expected demand patterns and size optimization, so a store receives a pack that matches what it’s likely to sell.
Allocation Across Every Channel
Store, DC, and digital inventory planned together.
Store, distribution center, and digital inventory unify into a single position, so an allocation decision for a ship-from-store order and one for a wholesale account both draw from the same current inventory levels.
Inventory distribution accounts for stores fulfilling online orders and shows how each store’s fulfillment work affects its allocation.
A planner can tailor allocation logic by channel, such as tighter reorder points for a fast-moving digital channel or longer lead times for a wholesale account, with the same automation running all of it.
Layered, Network-Aware Placement
Placement decisions that hold up even when one part of the network is constrained.
Every allocation decision weighs backorder status, safety stock position, margin, and transportation cost together, so each unit goes where it earns the most.
When outbound capacity at one DC is constrained, the system re-prioritizes the most critical units to the locations that need them most, holding network-wide service levels steady.
Post-launch performance feeds back into the plan continuously, combining internal and external data so planners can refine their allocation strategies and keep a pre-season allocation in step with sell-through all season long.
The Only One Recognized as a Leader in All Four
“Provides great visibility and is great for cost reducing feature such as route optimization and load consolidation.”
Gartner Operations Manager - Media
Progress and Results
In this testimonial, Kevin Volk, Vice President of Supply Chain at KeHE, shares insights into how KeHE leverages Manhattan TMS and DFIO solutions to optimize its supply chain operations. Kevin highlights the significant improvements in efficiency, on-time performance, and cost management since implementing these solutions. Kevin also discusses the strategic benefits of integration, data-driven decision-making, and future plans for expanding their use of Manhattan's capabilities.
“Provides great visibility and is great for cost reducing feature such as route optimization and load consolidation.”
Ann-Marie Daugherty
Vice President Of Logistics | Giant Eagle
Built for Your Role
Fulfill Your Potential With Manhattan
See how companies like yours use the TMS
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Giant Eagle is a leading regional supermarket chain.
From planning to execution, see how Manhattan’s solutions drive measurable improvements. The full case study shows what success can look like in action.
Allocation
Frequently Asked Questions
It’s the step that decides which store, channel, or DC each unit of inventory goes to, and how much, turning a forecast into a distribution plan. In supply chain management, it sits between forecasting and replenishment, placing the first units that later restocking builds on.
It makes those placement decisions from current demand. Manhattan’s allocation software uses size curves, store clustering, and current demand to place stock where it’s most likely to sell. It’s part of Manhattan Active Supply Chain Planning, alongside demand forecasting and replenishment.
Allocation is the initial placement decision: which location gets which unit, and how much of it. Replenishment keeps that inventory topped up afterward, using safety stock and lead times to trigger the next shipment. Manhattan runs both against the same plan.
Demand planning predicts how much you’ll sell; allocation decides where that inventory should physically go. Manhattan’s allocation builds on the same forecast, so the forecast accuracy you gain in planning carries straight into placement. For short-lifecycle products with no history, machine learning finds the demand patterns of similar items.
Allocation decides where inventory should go; a WMS executes the pick, pack, and ship work once it gets there. Manhattan’s allocation software accounts for real operating conditions, such as lead times and shipping costs, so the plan it hands to a WMS is one the warehouse can execute.
Yes. Allocation integrates in real time, through microservice APIs, with Manhattan Active® Order Management or any order management system, so allocation and order fulfillment share one view of inventory across channels.
Yes. Manhattan’s allocation software pulls sales, inventory, and product data from the ERP you already run and works alongside it.
Your Logistics Partner.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Eyebrow
Related Articles
What Veridian and Manhattan has been working on
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
June 23, 2026
Building the Next Generation of Supply Chain Experiences
Explore the technologies, partnerships and operational strategies shaping the next generation of connected commerce.
July 14, 2026