Why Omnichannel Demand Planning Keeps Blowing Up Your Forecasts

The Forecast That Looks Smart Until Reality Shows Up

On paper, your demand plan probably looks very civilized. There are rows, columns, and maybe even tasteful color coding. You line up last year's sales, sprinkle on some growth assumptions, factor in a promo or two, and call it a day. Then the real world shows up. TikTok wakes up one of your SKUs at 2 a.m., Target drops three surprise POs, Amazon doubles weekly velocity, and your D2C store politely keeps selling through all of it. Suddenly your beautiful forecast looks less like a plan and more like fiction.

This is the heart of omnichannel demand planning. You are not just guessing how much to buy. You are guessing how much to buy and where it needs to sit, which channels will eat first, and how fast everyone will change their minds. In the last couple of years, brands selling across four or more channels have seen much bigger week to week swings than their single channel cousins. The math did not get harder. The behavior did.

The uncomfortable truth is that your spreadsheets are not broken. They are just trying to describe a world that refuses to sit still. If your planning tools are built for yesterday's single channel life, they will keep betraying you in today's omnichannel mess.

How Channels Quietly Sabotage Each Other

Demand planning once revolved around a single forecast built on historical averages and seasonal adjustments, but omnichannel commerce fractures that simplicity because each channel now creates its own demand signal with its own timing, incentives, and failure modes. Amazon can surge based on algorithmic placement or a competitor stockout, retail orders arrive in large, inflexible blocks that ignore your internal plans, and your own site can rewrite demand overnight with a promotion that marketing scheduled without regard to inbound timing.

The sabotage is rarely intentional. Each channel behaves rationally within its own logic, yet those logics collide inside a shared inventory pool where acceleration in one place quietly starves another. By the time teams notice the conflict, the damage is already baked in: partial fills, missed SLAs, and reactive transfers that solve one problem by creating two more.

Where Traditional Planning Tools Give Up

Most planning tools were designed for environments where demand changed slowly and channels behaved independently, which means they rely on delayed signals, periodic updates, and assumptions that inventory states remain stable long enough for plans to matter. In omnichannel operations, those assumptions fail because inventory is constantly reallocated in response to live demand rather than static forecasts.

The result is a planning lag that compounds. Forecasts are built on yesterday’s data, replenishment follows last week’s reality, and by the time the plan registers a spike or shortfall, inventory has already moved elsewhere. Tools do not break loudly; they simply stop being useful at the speed modern commerce demands.

How G10 Builds Planning On One Version Of Reality

G10 starts with inventory truth rather than forecast optimism. ChannelPoint, the proprietary WMS, tracks every unit by scan across receiving, storage, picking, packing, and shipping, which eliminates approximation and removes the need to reconcile multiple “almost right” numbers. As Bryan Wright, CTO and COO, puts it: “A good WMS tracks inventory through the warehouse at every point you touch it. At any moment I know where the product sits, how it moved, and what it is connected to.”

That single version of reality changes planning behavior. Forecasts stop being abstract exercises and become operational hypotheses tested against live inventory, live orders, and live constraints. When demand shifts, the system reflects it immediately, allowing teams to adjust allocation before conflict turns into failure.

Planning Across A Distributed Warehouse Network

G10’s planning advantage compounds across its distributed warehouse network, because facilities in Wisconsin, South Carolina, Texas, Arizona, and Nevada all operate on the same WMS with the same data model. Inventory does not disappear into regional silos; it remains visible, comparable, and movable within a unified system.

As Holly Woods, Director of Operations, explains, “Having strategically placed warehouses lets inbounds come in faster, which means we can get it distributed faster.” That speed matters because it shortens the gap between inbound supply and outbound demand, reducing the window where forecasts drift away from reality.

People Who Actually Read The Numbers

Systems alone do not plan demand; people interpret signals and decide when to intervene. G10 pairs its technology with direct human oversight so anomalies are noticed early rather than after service levels degrade. As Joel Malmquist, VP of Customer Experience, says, “Every account at G10 has a direct point of contact. You only have to reach out to one person.”

That structure matters because omnichannel planning failures often look small at first: a channel drawing down faster than expected, a region lagging behind, a promotion outperforming its forecast. Having someone responsible for seeing and acting on those signals keeps problems contained while options still exist.

Demand Planning For Brands That Expect Growth

G10 was built for brands that expect complexity to increase rather than smooth out over time. Founder and CEO Mark Becker shaped the company around the reality that growth multiplies channels, speeds decisions, and narrows tolerance for error, which means planning systems must absorb pressure rather than demand perfect forecasts.

For brands preparing to scale, demand planning cannot depend on polite channels or cooperative timing. It has to survive omnichannel chaos by anchoring decisions in real inventory, real movement, and real constraints, which is where G10 turns forecasting from a hopeful exercise into something that actually works.