By Eugene Amigud, Chief Product & Innovation Officer, Infios
Traditionally, supply chain planning has been based on stable assumptions about demand, lead times, and costs. But despite heavy investment in AI forecasting, demand sensing, and predictive analytics, unplanned supply chain incidents still cost businesses $184 billion annually, according to the J.S. Held Global Risk Report 2025.
Many leaders assume the problem stems from flawed data and inaccurate models. But recent risk analyses show disruptions are the norm, not the exception.
The first major challenge to the forecasting framework was COVID-19, which wreaked havoc with the global supply chain for everything from flour to semiconductors. In 2025, the sudden implementation of tariffs by the U.S. upended the board yet again. In 2026, the closing of the Strait of Hormuz due to hostilities between the U.S. and Iran has disrupted the transport of multiple commodities, hurting many sectors in global trade, including energy, agriculture, and manufacturing.
In this unpredictable environment, even the most sophisticated forecasting models fall short. Forecasts depend on patterns. Geopolitical conflict, climate events, regulatory change, and cyber attacks have little historical precedent to model against, which is exactly what makes them so hard to forecast.
In addition, supply chains are now subject to several layers of risk at the same time. One disruption is enough to test a system, but these events often interact, creating a cascade of failures that is difficult, if not impossible, to recover from.
Most companies are running on technology infrastructure that was never designed for this degree of chaos. Their operating models were built for linear plans instead of nonlinear shocks. And even the most sophisticated forecasting model produces insights no one can act on fast enough: Decision-making is slow, execution is fragmented, and the gap between knowing that something changed and doing something about it is too wide.
Meanwhile, changes continue to unfold at all times, keeping teams stuck in reactive mode as they try to keep up. And as the burden of making decisions and synchronizing action falls on overwhelmed operators, risks and costs increase. Nearly 60% of supply chain leaders say manual workflows and interventions are the biggest source of inefficiency in their operations.
Their mistake is to treat these disruptions as surprises instead of assuming they will happen. That shift from prediction-led planning to execution-led adaptability is the real opportunity. This mindset moves resilience to the center of any solution, asking not “What will happen?” but “How fast can we respond when it does?”
A New Model for Performance
With traditional forecasting, by the time businesses catch up with major disruptions, inventory has already been delayed, orders have been rerouted, and customer commitments are at risk. Supply chain leaders are clear on the better strategy: 79% say the biggest advantage comes from how quickly and dynamically they can adjust their execution and plans. The key performance metric today is not forecast accuracy but time to adapt: measuring how long it takes your company to detect disruption, respond to it, and recover from it.
Managing this resilience means moving to a new operating model—intelligent supply chain execution—that supports:
- Real-time data integration and shared operational context instead of static forecasts. Incorporating information about what is actually happening, instead of relying on point-in-time planning or historical patterns, creates a continuously updated planning environment that enables more-informed decisions.
- AI decision making at the execution layer. Instead of focusing AI solely on forecasting or analytics, companies should embed it directly into operational workflows. Beyond the ability to anticipate disruptions, this enables intelligence that understands the full impact of sudden shocks across the supply chain, and the capability to take autonomous action across systems, with guidance and oversight from operational teams.
- Cross-functional workflows that can adjust while conditions are in motion. Companies need technology that connects and coordinates execution across orders, warehousing, and transportation as one system. Businesses gain the ability to respond to disruptions holistically, eliminating the need for inefficient manual interventions and disconnected, ineffective problem-solving efforts.
Intelligent supply chain execution depends on a foundation of governance that holds control while increasing speed—with policy-driven frameworks and guardrails for AI autonomy.
From Forecasts to Data-Driven Action
When disruption is constant, forecasting, no matter how advanced, and traditional supply chains that rely on fragmented systems operating independently cannot keep up. Intelligent supply chain execution supports this shift by turning disconnected components into a coordinated system of action.
Forecasts will always be useful, but they are no longer enough on their own to keep an organization online. As disruption remains a permanent feature of the global economy, organizations need systems that can continuously sense, decide, act, and learn from each disruption, in real time.
Businesses that incorporate this model won’t just weather uncertainty; they’ll be poised to thrive in an increasingly unpredictable environment. And only by designing for constant surprise can they close that $184 billion gap.
Infios AI connects orders, warehousing, and transportation, turning fragmented signals into coordinated action—sensing disruption as it happens, deciding the right response and executing it in real time, and then learning from every outcome to improve the next one. That intelligence operates inside existing workflows, with the governance and guardrails leaders need to move from manual firefighting to continuous controlled execution.
Ready to move from prediction to action? Learn more about how Infios can build execution-led adaptability into your supply chain.
