For years, statistical forecasting was the trusted foundation of supply chain planning. The world was more predictable, patterns repeated, and “what happened before” was usually a decent way to determine “what will happen next.” But that world no longer exists. Demand shifts overnight. Promotions, weather events, and viral trends can upend forecasts in days. Historical patterns still matter, but they’re not the full story anymore.
When volatility spikes, statistical models that depend on stable, repetitive data fall apart. Every misread signal ripples through production, inventory, and service levels, turning what used to be small forecasting errors into costly disruptions. Today, we’re no longer operating in a system of averages. We’re operating in a system of exceptions. That’s where machine learning changes the game.
Moving from historical data to future-focused predictions
Rather than relying solely on what’s happened before, machine learning continuously learns from what’s happening now. It takes foundational data that typically aren’t included in statistical forecasting models, like incorporating product attributes such as size, type, and color, to better predict demand patterns.
It can also pick up on subtle shifts, like changes in customer behavior, emerging market trends, and external signals like point-of-sale data or promotions, and adjusts forecasts on the fly. This enables companies to adapt in real time, capturing measurable gains early while their forecasting continuously learns and adapts to new circumstances.
An essential piece of this forecasting process is concurrency. Even connected systems can lag when data moves sequentially — from demand to supply to inventory and back again. That delay can turn insight into hindsight.
[Read more: ML demand forecasting]
Concurrency: The real-time multiplier
Concurrency eliminates that gap. It allows every function to see and respond to change at the same time, from the same data. A shift in demand instantly ripples through production, logistics, and procurement. Teams don’t have to wait for updates because they move together.
In a concurrent planning environment, forecasting becomes part of a real-time conversation across the business where decisions happen in harmony instead of in sequence. When every function operates from one synchronized source of truth, forecasting empowers orchestration rather than guesswork.
Using AI to improve, explain, and build trust
Another essential piece of this improved forecasting is explainability. Your teams can only work and plan together if everyone trusts AI to provide reliable answers, which means it’s essential to understand what is happening and why. One of the biggest challenges with AI forecasting has always been the “black box” problem: if no one knows why the model predicts something, how can teams act on it with confidence? That’s why AI-powered forecasting must include clear, data-backed insights everyone can review. Teams can then assess the value and fit of recommendations, improving speed, clarity, and alignment around decision making.
[Read more: Reimagining demand planning with predictive AI]
Achieving agile, confident decision making
In a world where change is constant, speed and confidence matter as much as precision. The most successful organizations aren’t necessarily the ones with the “perfect” forecast. They’re the ones that can respond fastest when reality changes. Machine learning demand forecasting, when connected across the business, gives planners that edge. It creates forecasts you can trust and act on in real time.
Forecasting will always be about anticipating what’s next. But it’s time to move beyond relying on historical data for predictions. The pace of change isn’t slowing — and neither should your forecasting.
Kinaxis ML demand forecasting is built on Maestro™, the only end-to-end supply chain orchestration platform that infuses AI to enable faster, smarter supply chains that perform in complete harmony. Learn more here.
