Why Forecasting Still Fails and How AI Is Now Changing the Math - ToolsGroup

Every supply chain leader knows the pain of forecasts that miss the mark. Single-point predictions fail in the face of volatile demand, leaving teams stuck with either excess stock or costly shortages. Planners lose hours chasing down exceptions, executives lose trust in the numbers, and customers lose patience when product availability slips. The industry has long tried to patch over these failures with manual overrides, buffer stock, or IT workarounds—but these fixes only buy time.

One answer now gaining traction is probabilistic forecasting, which models a range of outcomes instead of one fragile prediction. Jeanette Barlow, Chief Product Officer at ToolsGroup, argues this shift allows planners to balance service levels against carrying costs with mathematical precision. By using probability distributions rather than static forecasts, companies report higher product availability and dramatic reductions in manual workload.

Jeanette Barlow, Chief Product Officer at ToolsGroup

“Probabilistic forecasting makes it possible to make smarter assortment and inventory decisions. This directly improves service levels, reduces working capital, drives revenue growth, and automates complex processes to boost productivity. Our customers see real results, fast. They often report a 3–5pp increase in forecast reliability, 95–99% product availability, and a 50–90% reduction in planner workload with machine learning automation.”

Integration is another barrier that often sinks new planning tools. ToolsGroup’s platform connects with SAP, Oracle, and Microsoft Dynamics 365, along with order and warehouse systems, without requiring firms to rip out existing IT. This lowers adoption risk, a key reason many planning transformations stall.

Looking forward, explainability is becoming as important as accuracy. Planners and executives want to know not just the recommendation but the assumptions behind it. Barlow stresses the need for models that can be stress-tested with “what-if” scenarios and clearly show how inputs drive outputs. That transparency is critical to winning organizational trust.

The competitive field is heating up. o9 Solutions, Blue Yonder, and Kinaxis are all pitching similar blends of AI-powered planning, integration, and scenario modeling. Each argues it can deliver resilience and agility in markets where volatility is the norm, not the exception.

For practitioners, the takeaway is clear: forecasting will never be perfect, but the companies that treat uncertainty as data to be modeled—not noise to be ignored—are already pulling ahead. The question for leaders is whether their systems still cling to outdated single-point forecasts, or whether they are ready to let AI re-write the math.

For tips, leaks or anonymous sourcing: editor@thesupplychainer.com