From inflation to geopolitical conflicts and weather events, myriad invisible forces shape your customer demand every minute, every day.
Today, every supply chain faces increasing demand volatility, regardless of industry. The potential of AI to improve forecast accuracy under these uncertain conditions is enormous.
But a recent study by McKinsey revealed that only 9% of companies are actively using AI in the supply chain today. Only 2% have scaled it anywhere in their supply chain planning or operations.
What’s holding companies back? It’s not the technology.
Much of AI's impact on demand forecasting comes from machine learning (ML), the subset of AI that enables systems to learn from data and continuously improve over time. Fueled by ML, today’s advanced demand planning engines excel at identifying patterns and exceptions across thousands of products and customers simultaneously.
ML applies these insights to improve forecast accuracy, reduce bias, and uncover demand drivers. As new data becomes available, ML models automatically learn and adapt, helping organizations uncover new insights and continuously improve forecast performance.
With the technology ready, what’s keeping so many companies from embracing AI for demand planning? And why are so many efforts stalled at the pilot stage? In our experience, there are three keys to a successful implementation of ML demand forecasting, across industries and business models.
1. Stop chasing every possible dataset. Your data already unlocks the insights you need
We’ve noticed that many forecasting teams are held back by a fear that they don’t have enough data to gain value from ML forecasting. But most companies already have access to the critical information needed by ML. There’s no need to acquire new datasets to get started with ML-enabled forecasting.
Historical sales, product and customer attributes, calendar data, and forecast history provide a rich set of information about how demand behaves across products, customers, channels, and time periods. This data is more than enough to begin realizing meaningful improvements with ML forecasting.
While your existing data may seem simple on the surface, it reveals complex relationships that influence demand, including seasonality, customer buying patterns, product affinities, lifecycle trends, and other demand drivers that can be difficult for traditional statistical models to capture.
That doesn't mean external signals such as promotions, weather, or point-of-sale data aren't valuable. In many cases, they’re important demand drivers. But you don’t need to wait until every possible dataset is available before getting started with ML forecasting. Leading companies often begin with a strong foundation of planning data, then selectively incorporate additional signals based on forecast performance and business context.
At Kinaxis, we meet customers where they are. ML demand forecasting is embedded directly in the Maestro platform and can be launched quickly using your existing data, without requiring external datasets.
2. If AI isn’t explainable, it won’t be adopted. Make the logic transparent
One of the major benefits of ML is that it makes the forecasting process more dynamic, more automated, and self-learning. Frequent manual inputs and human interventions are significantly reduced. But this is only possible if team members see and trust the logic behind ML.
Explainability helps planners understand which demand drivers are shaping the forecast, as well as exactly how these drivers influence specific forecast changes.
If your planners don’t trust the decisions made by ML, they won’t use it. They’ll continue to rely on best guesses or intuition, making ML a limited analytics tool and shortchanging its true power. Many companies are stalled at the pilot stage because the logic of ML remains a black box to their planners. When AI is not explainable, planners often override its decisions — losing time, eroding profits, and damaging customer relationships.
Explainable AI ensures planners still see and understand the big picture. With Kinaxis, ML-generated forecasts are shown in a clear, explainable context, with visibility into both the underlying demand driver and its predicted downstream impacts across inventory, supply, and production. Stakeholders fully understand what shifted, why it shifted, and what it means for the entire supply chain before committing to a response.
3. Leverage AI to link the forecast to execution plans, business outcomes, and financial risks
Even with the most advanced demand forecasting process, the hard part is what happens next: Turning a forecast into smart operational decisions fast enough to matter. ML demand forecasting only realizes its true potential if it’s directly linked to business decisions and outcomes.
If you’re forecasting with ML, but that process is managed by a separate team, using separate software, chances are you can’t execute on those forecasts quickly. Forecasts need to be explicitly connected to everyday supply chain operations, with execution that’s aimed at a specific, predefined business outcome. And every forecast update must immediately reveal a downstream impact, so teams can act right away.
With its concurrent planning capabilities, Kinaxis Maestro ensures any corrective actions are rolled out across the supply chain immediately, while a full range of responses is still within easy reach. Maestro seamlessly orchestrates the end-to-end supply chain, re-aligning all assets, and adjusting all workflows as demand inevitably ebbs and flows.
Maestro also significantly reduces risk exposure in your supply chain operations, as more accurate forecasts help you reduce excess inventory, improve service levels, minimize costly expedites, and free up working capital.
As just one example, a leading high-tech contract manufacturer used ML demand forecasting from Kinaxis to help achieve an 11% reduction in forecast errors — which translated into a $28 million savings in inventory avoidance. The company began with its existing data for 15 major customers, representing $3.8 billion in revenue. Based on these early results, the manufacturer is currently scaling ML demand forecasting across additional customers. This Kinaxis customer is proving that forecast improvements translate directly into measurable business value.
Get started today by partnering with Kinaxis
Right now, you’re probably investing a lot of time improving a poor initial forecast with manual changes, battling to reach consensus, and trying to keep up with every new data point. If you’re using AI at all, you’re most likely struggling to gain your planners’ trust in its decisions — as well as link those decisions to rapid execution. Meanwhile, demand has already shifted, and now you’ve missed sales opportunities while your inventory ages.
But what if you started with a very strong initial forecast that cuts through the noise, focusing only on the most impactful signals and patterns? What if demand changes were surfaced instantly, trusted by your team, and confidently and concurrently executed across the supply chain? See what this looks like for your organization.
