Smarter Orders, Stronger Margins: How Intelligent OMS Drives Profitability at Scale
Why AI-powered orchestration, real-time inventory, and composable architecture are reshaping the role of OMS in modern retail.
Margins are shrinking. Fulfillment costs are rising. And customer expectations? Through the roof. For digital retail executives—especially those in complex environments like home improvement, furniture, and large-format retail—order management has moved from back-office function to front-line margin lever.
Unfortunately, traditional order management systems (OMS) were not built for this moment. Used to playing a supporting role, they rely on batch inventory data, rigid routing logic, and manual exception handling. That might have worked when supply chains were stable and customer patience longer, but today these systems are holding businesses back. Being a passive record-keeper won’t cut it anymore.
A next-generation OMS that combines real-time inventory visibility with AI-driven decisioning is not just an asset, it’s a strategic imperative. Solutions like Fluent Order Management allow retailers turn operational chaos into competitive advantage by dynamically optimizing fulfillment, scaling smarter, and minimizing costly inefficiencies to protect—and expand—gross margin.
What’s at Stake: OMS and Margin Erosion
In retail, fulfillment is one of the largest drivers of variable cost. Every late delivery, split shipment, or suboptimal routing decision chips away at the margin on an order. Worse, traditional OMS setups are often blind to the cumulative effect of these decisions. They route based on static rules, not margin impact.
When inventory data is updated in batches, it’s almost always out of sync with reality. A system might “think” an item is in stock even after it’s been purchased. Routing logic is typically hardcoded, which means when disruptions like bad weather, a warehouse slowdown, or sudden demand spikes occur, it can’t adjust. And when systems don’t talk to each other, operations teams are forced to work around them, introducing additional costs, unnecessary risks, and unforeseen delays.
These aren’t edge cases; they’re daily occurrences in retail. And each one erodes margins, customer satisfaction, and the ability to scale.
By contrast, an intelligent OMS surfaces the true cost-to-serve—helping digital leaders make smarter tradeoffs in real time. Whether it’s deciding to ship from a store closer to the customer or delay fulfillment slightly to avoid a costly expedited shipment, these micro-decisions add up to significant margin preservation over time.
When fulfillment is treated as a strategic lever—not just a logistics function—margin performance follows.
A New Approach: Intelligent OMS
The new generation of OMS platforms takes a fundamentally different approach. Rather than relying on stale data and static rules, they operate on real-time signals and AI-powered orchestration. They treat inventory not as a snapshot, but as an interconnected, constantly shifting network of availability, movement, and constraints.
This shift enables dynamic order orchestration. By combining real-time inventory visibility with AI, they orchestrate fulfillment based not just on availability and speed, but on profitability. AI evaluates multiple variables—inventory levels, shipping costs, customer location, warehouse performance, even weather forecasts—to determine the best way to fulfill each order. And because these decisions are made in real time, the system can reroute on the fly when conditions change.
This enables margin-sensitive fulfillment. For instance, a modern OMS might decide to consolidate items at a single location to avoid a split shipment that would double last-mile costs—or reroute an order to a location with lower labor cost and higher throughput.
These are small, automated decisions made at scale—and their collective impact on margin is substantial.
These aren’t theoretical capabilities; they’re being deployed now by leading retailers.
Consider the case of ALDO Group, which used Fluent Order Management to gain real-time visibility across its inventory network. By consolidating data from stores and warehouses, ALDO reduced cancellations, improved pick times, and built a more scalable fulfillment strategy.
Or another Fluent customer, fast-growing fashion brand Psycho Bunny, which used AI-driven sourcing to minimize split shipments by over 50%, resulting in a 33% reduction in average time to deliver while lowering shipping costs.
And then there are the retail giants.
Walmart applies AI to forecast demand based on hyper-local data and adjust replenishment in real time. During the busy holiday season, they were able to pre-position high-selling items like toys and electronics at regional warehouses to support faster replenishment of these in-demand goods. Grocery chain Aldi is renowned for its no-frills emphasis on an efficient, cost-effective shopping experience. Aldi leverages predictive analytics to align inventory, logistics, and customer behavior—like bringing more organic produce into stores where the clientele has more appetite for it—increasing both efficiency and responsiveness.
Each of these examples demonstrates how a modern, intelligent OMS can translate directly into measurable business outcomes: higher margins, faster fulfillment, and improved customer satisfaction.
Why Does Composable Architecture Matter in OMS?
While intelligence drives smarter decisions, flexibility is what makes those decisions sustainable. Smarter decisions only work if your systems can act on them quickly. That’s why composable architecture is foundational to the modern OMS.
Built on modular components and open APIs, composable OMS platforms allow retailers to evolve without overhauling. Rather than replacing legacy systems wholesale, you can incrementally add capabilities—like store fulfillment, dropship support, or real-time inventory feeds—without destabilizing the core.
This flexibility enables fast adaptation to seasonal shifts, promotional campaigns, or disruption scenarios, ensuring that OMS isn’t just technically agile—but margin-agile. It lowers transformation risk while accelerating time to value. Composable architecture ensures that your OMS can adapt as your business does, whether that means scaling to new markets, launching new channels, or navigating the next disruption.
How should digital leaders approach OMS transformation?
For executives overseeing digital transformation, OMS is no longer just a backend concern. It’s central to delivering the kind of agile, efficient, and customer-centric commerce experiences the market demands.
To move forward, ask yourself these critical questions:
- Inventory visibility: Do you have real-time inventory visibility across all channels and fulfillment nodes? Can you track and act on true cost-to-serve across channels, fulfillment nodes, and inventory sources?
- Decision-making logic: Are your fulfillment strategies dynamic and data-driven, or hardcoded and manual? Is your current system prioritizing margin, or just speed and availability?
- System flexibility: Can your OMS adapt as your business evolves, or is it locking you into yesterday’s capabilities? How quickly can you adjust fulfillment strategies as market or cost dynamics shift?
If your answers to those questions raise doubts, it’s time to re-evaluate your OMS foundation. An intelligent, composable, real-time OMS doesn’t just improve operations. It protects your margins, accelerates your growth, and gives you a sustainable edge in a volatile market.
Leigh Bryant
Editorial Director, Composable.com
Leigh Bryant is a seasoned content and brand strategist with over a decade of experience in digital storytelling. Starting in retail before shifting to the technology space, she has spent the past ten years crafting compelling narratives as a writer, editor, and strategist.