Why Is Demand Forecasting Software Leaking Margin in the US and What to Do About It?

Demand Forecasting

Manufacturers based in the US are lagging in tracking their supply chain closely. According to an industry benchmark, nearly half of all demand predictions made by manufacturing businesses are wrong.

And the uncomfortable reality is that most organizations don’t know the true scale of the problem because they’re measuring forecast accuracy at the aggregate level, not where it matters at the SKU and location level. The gap between boardroom-reported accuracy and operational accuracy is routinely 30 percentage points.

This isn’t a data problem but a structural one, and it’s costing US manufacturers more than they realize.

**Saxon’s supply chain AIssist** applies advanced ML models to forecast demand at the SKU level, incorporating historical data, seasonality, promotions, and regional trends.

The Real Cost of Broken Demand Forecasting Software

For a $500M manufacturer, moving from average to best-in-class forecast accuracy releases **$15–25M in working capital**. Annual expedite and markdown costs alone drop by $8–15M at the same revenue baseline. These projections reflect what happens when demand intelligence no longer lags reality.

Yet most US manufacturers are spending 60% of their time on data gathering, reconciliation, and exception management rather than making decisions. They’re buried in the process of forecasting rather than acting on forecasts. This results in an **8–12% inventory holding spike due to forecast latency.**

Demand forecasting software was built for a stable world. The statistical models of moving average, ARIMA, and exponential smoothing assume that the future resembles the past. These models fail if tariffs change dramatically overnight, an important supplier falls off the map, the ports close, or there is an unexpected increase in regional demand prior to the week when the plan is run. The assumption that past sales trends predict future sales behaviour assumes that tomorrow will be the same as yesterday, which might not always be true.

There’s also a subtler problem that rarely gets discussed: bias embedded in the process itself. When forecasts are informed by sales teams with optimistic pipelines or customer surveys skewed by extreme experiences, the inputs are compromised before any model runs. Clean data is the foundation, but most organizations haven’t solved for it.

Planning teams that are always reacting and decisions made under pressure result in the supply chain agility that exists on paper but not in practice.

What Modern Demand Forecasting Software Actually Needs to Deliver

Fortunately, the gap between average and world-class performance can now be closed, but only with the right methodology. For demand forecast management software in 2026 and beyond, some key advancements must be made:

Handle multi-sourced, real-time signals: Not just historical sales volumes, but also inventory levels, promotion schedules, lead times from suppliers, weather data, economic measures, and regional data, all at the same time. A sudden increase in demand shouldn’t influence forecasts for the entire upcoming period, only the short term.

Integrate natively into existing ERP environments: It’s well known that running an extra platform in parallel leads to low adoption rates and basically zero impact. The AI needs to fit into existing workflows, like SAP, Oracle, and other systems that drive daily outcomes

Support human decision-making, not replace it: When AI works like a black box, trust erodes fast. Users need explanations for the forecasts, and they’ve got to be able to challenge and tweak them based on their insights.

How Saxon AI Approaches Demand Forecasting Differently?

Saxon AI’s approach to demand forecasting was built around a specific insight: most AI implementations create shadow systems that sit alongside the ERP, generate forecasts no one fully trusts, and eventually get abandoned. Saxon builds AI that strengthens the enterprise backbone instead of breaking it.

AIssist use of such factors, manufacturers can use real-time dashboards to help them plan actionable insights to act on immediately. AIssist monitors demand signals and inventory positions, adjusting the plan within defined rules and flagging exceptions that can be reviewed rather than waiting for the weekly planning cycle.

Critically, this intelligence is delivered inside existing enterprise systems. Saxon’s AI integrates natively with SAP and other ERP environments, meaning planners consume predictions within the workflows they already use. Therefore, no context-switching, no parallel systems, and no adoption friction.

The platform also supports scenario planning at a level that changes how US supply chain teams respond to disruption. When a port closes, or a tariff doubles input costs overnight, teams working with Saxon aren’t starting from scratch. They’re already working from scenario plans built before the disruption hit.