.svg)
.svg)
Webinar: probabilistic forecasting in Supply Chain
In this masterclass webinar, Flowlity explains the probabilistic approach to forecasting: predicting a range of likely outcomes with their probabilities, so AI-driven planning can manage uncertainty instead of ignoring it.
Single-number forecasts hide risk. This session shows how probability distributions change safety stock, service level and planning decisions under volatility.
What this webinar covers
- The shift from deterministic to probabilistic forecasting
- How AI quantifies demand uncertainty
- What it changes for inventory and S&OP decisions
For the practical follow-through, our article on predictive analytics in Supply Chain shows how probability ranges reset safety stock levels.
Watch the webinar
Level up your supply chain with AI.
Get a demo
FAQ
Find everything you need to know right here.
Is probabilistic forecasting more complex to use?
No. Modern tools hide complexity and present insights in an intuitive way. Planners do not need to manipulate probability distributions directly: the platform surfaces the median forecast, the upper percentile and the recommended buffer for each SKU period, alongside clear exception alerts when something requires attention. The probabilistic logic runs underneath, while the planner interface stays close to familiar concepts such as service level, coverage and lead time. The benefit is that decision quality improves without retraining the team on new statistical methods. In practice, adoption tends to be faster than rule-based tools, because the recommendations align more naturally with how planners already think about risk.
Does it replace planners’ expertise?
No. It augments human expertise with better data and risk visibility. Planners remain in charge of strategic decisions, customer relationships and exception handling, while the model takes care of the repetitive calculations that no team can scale manually across thousands of SKUs and locations. The shift is from spending most of the day producing numbers to spending most of it interpreting them and acting on the few that matter. In practice, planners gain a clearer picture of demand uncertainty, lead time risk and inventory exposure per SKU period, which lets them apply their judgment where it has the most impact on service level and working capital.
Can probabilistic forecasting reduce inventory?
Yes. By sizing buffers according to real risk, not assumptions. Traditional safety stock rules tend to apply blanket coverage or rely on static parameters that age quickly under volatility, which often results in too much stock on stable SKUs and too little on variable ones. Probabilistic forecasting reverses that pattern by quantifying demand uncertainty per SKU period, so the buffer concentrates where it actually protects service and shrinks where it only ties up working capital. The net effect is lower total inventory at equal or better service level, with the additional benefit that the logic adapts continuously as demand profiles change.
