Why You Need AI Forecasting in Supply Chain to Weather the Storm

As of June 1 the 2025 Atlantic Hurrican Season has begun and the National Oceanic and Atmospheric Administration (NOAA) is sounding an alarm – and not just about approaching storms. The agency, responsible for tracking weather systems and issuing life-saving alerts, is struggling to staff its forecasting offices. Some are so critically short-staffed that NOAA is offering relocation incentives just to keep 24-hour operations afloat. In places like California’s Central Valley and eastern Kentucky, overnight shifts go uncovered, raising fears about how prepared we are for unpredictable weather events. More than a cautionary tale for emergency preparedness, this story mirrors what’s happening across supply chains worldwide.

Just like NOAA, supply chain organizations face an urgent dilemma: How do you continue to predict, plan, and respond in a world of rising complexity and the challenges of finding and retaining top human talent? The answer lies in embracing automation, AI-driven forecasting in supply chain planning software, and new planning paradigms that transform how work gets done.

More Data, Fewer People

NOAA’s forecasting isn’t simple. Meteorologists must synthesize vast amounts of data like satellite imagery, temperature trends and atmospheric pressure changes, using multiple models to anticipate the trajectory, duration, and severity of weather events. Increasingly, they rely on probabilistic forecasting, which models a range of potential outcomes rather than a single deterministic prediction. It's more advanced, but it’s also more demanding, both in computational and human expertise.

Supply chains are heading down a strikingly similar path. As demand signals become more volatile and supply networks more intertwined, planners must parse through torrents of data. Global disruptions, shifting consumer preferences, climate impacts, and geopolitical and trade uncertainty have increased the pressure to deliver more accurate and nuanced forecasts. But just as in NOAA’s case, the number of skilled professionals who can manage this complexity is shrinking.

Aging populations, declining birthrates, and evolving workforce expectations are reshaping the global labor landscape. According to recent research, the competition for skilled supply chain talent is intensifying, driving up hiring and retention costs and putting strain on traditional workforce models.

When the number of available experts dwindles and the complexity of decision-making escalates, it becomes critical to rethink how work gets done. Organizations can no longer rely on large teams of planners to crunch numbers and tweak models-nor should they. The future belongs to leaner teams, augmented by intelligent systems.

The Need for Smart Automation

In this emerging paradigm, planning roles are transforming. We’re moving away from large teams of operational planners toward a model where a smaller group of high-impact individuals — insight originators or plan orchestrators — drive the outcomes. These individuals aren’t bogged down by manual processes; instead, they work in tandem with advanced automation and AI to handle routine and repetitive tasks and computational heavy lifting.

To enable this shift, organizations must embrace  “touchless forecasting,” which provides a unique scalable automation opportunity within demand planning. This concept leverages AI and machine learning to automate baseline forecasting, freeing human planners to focus on more strategic tasks like scenario planning and risk assessment.

Why AI Forecasting Matters

According to analysts, by 2030, 70% of large organizations will have adopted AI/ML-based forecasting to predict future demand. And for good reason. Traditional time-series models have reached their limits. They are rule-based, deterministic, and struggle to keep up with the fast-changing signals of today’s market.

AI forecasting, on the other hand, thrives in complexity. It can leverage hundreds of demand drivers, from weather patterns to social media data, and deliver range-based probabilistic forecasts that give planners a richer understanding of risk and variability. This is crucial in a world where “accurate” is more than hitting a number – it’s about understanding the range of what might happen and being prepared for it.

Supply chain leaders must adopt tools that provide dynamic visibility into a range of demand outcomes, so they can prepare adaptive responses, rather than one number, static plans.

The Convergence of Complexity and Constraints

NOAA’s challenges highlight the broader shift confronting supply chain leaders: a convergence of rising complexity and diminishing human capacity. This is a strategic inflection point. Those who continue to rely on traditional models and legacy forecasting approaches risk being outpaced by competitors who can see further, faster.

Automation and AI in supply chain are not future ambitions – they are current imperatives. And the organizations that thrive will be those that use these tools to leap ahead.

Plan for a Weather-Ready Supply Chain

Just like NOAA’s mission is to prepare a “weather-ready nation,” companies must strive to build more resilient, responsive supply chains. The storms we face may differ from those NOAA tracks, but the planning challenge is fundamentally the same.

For supply chains to be ready for anything, it’s key to rethink the role of human expertise, invest boldly in automation, and embrace AI forecasting that reflects the dynamic world we operate in.

Let’s talk about how to best navigate uncertainty. Unlike the weather, how we respond is entirely within our control. And now is the time to act, before the next storm hits.