Mar 26, 2026 Stefan Auerbach
ShareVolatile markets, short response times, and a growing number of external factors characterize everyday operations in logistics and supply chain management. Decisions regarding inventory, delivery capacity, or purchasing volumes must increasingly be made under conditions of uncertainty.
For decision-makers, this means that forecasts are no longer purely an operational issue. They influence investments, delivery capacity, capital tied up, and thus financial accountability. At the same time, they are among the areas where risks are difficult to grasp.
Many demand forecasts are still based on Excel spreadsheets or standard functions from ERP systems. These methods extrapolate historical data and deliver reliable results when demand trends are stable. Under volatile conditions, however, they reach their methodological limits. Short-term fluctuations cannot always be clearly distinguished from genuine trend shifts.
A major customer orders significantly larger quantities than usual over several months because its sales have increased. A traditional forecast might interpret this as the start of a new demand trend and adjust the forecast for the coming months accordingly. However, the forecast figure alone does not reveal whether this is actually a sustained change in demand or merely a short-term increase in sales. Planning teams must therefore subsequently evaluate this uncertainty manually.
External events or abrupt changes in demand often only become apparent once they have already been reflected in the historical data. Traditional forecasts continue to provide value in such situations. What is often missing, however, is transparency regarding how reliable this value is and what risks lie behind it.
A beverage manufacturer is forecasting summer demand for a soft drink: new competitors, unpredictable weather, and short-term promotions are disrupting previous patterns. While traditional methods still appear to deliver accurate forecasts, they fail to adequately capture the actual uncertainty.
With volatile demand, traditional forecasting methods naturally reach their limits and often fail to adequately highlight uncertainties. A pre-trained AI model complements this perspective by recognizing—based on empirical data from millions of time series—when no stable patterns exist. In such cases, the AI forecast provides an interval that reflects the realistic range of demand. This creates greater transparency regarding risks and a more solid foundation for decision-making.
Planning teams compensate for this uncertainty with experience, additional analyses, and manual assessments. This presents a key challenge for decision-makers: not merely to review forecasts, but to systematically evaluate their validity.
Especially in dynamic markets, it’s not the perfect number that matters, but whether a forecast makes risks transparent and enables sound decision-making.
Decision-makers therefore need additional perspectives beyond mere numerical values:
This shifts the focus: forecasts must increasingly be conceived from a decision-making perspective.
This is where advanced AI-based forecasts demonstrate their added value. Instead of merely providing numerical values, they map out realistic ranges of expectation with clear indications of bandwidths and probabilities. The underlying models are trained on many different demand patterns and can therefore classify new demand developments more quickly and robustly.
At the same time, relevant demand signals are reliably separated from statistical noise, ensuring that short-term fluctuations are not prematurely interpreted as trends.
Furthermore, modern forecasting models can account for complex influencing factors—such as calendar effects that shift demand over time.
A confectionery manufacturer sells significantly more products around Easter every year. Since the date of the holiday shifts annually, the timing of the peak in demand also changes. Forecasting methods that rely heavily on the previous year’s sales figures can only account for this shift to a limited extent.
AI forecasts can account for these external variables and significantly improve forecast accuracy.
AI-based forecasting models automatically detect such effects and correctly identify the timing of the demand peak in the calendar. This allows for much more precise planning of seasonal demand peaks. Human judgment remains central to the process but is supported by reliable decision-making criteria.
For decision-makers, a forecast only realizes its full value when it supports operational, tactical, and strategic questions in equal measure.
Advanced AI-based forecasting services enable:
This creates a consistent foundation for decision-making across the entire supply chain—from daily planning to strategic management.
When uncertainty becomes visible and can be prioritized, day-to-day operations change:
Planning becomes calmer, more focused, and more strategic.
In stable markets, traditional forecasting methods deliver reliable results.
But the more dynamic the environment, the more important it is to take a broader view of forecasts.
Advanced AI-based forecasts strengthen decision-making confidence, especially where uncertainty cannot be avoided but must be actively assessed. They make risks, ranges, and structural changes transparent, thereby creating the foundation for transparent, robust decisions.
It is not the forecast that determines a company’s success, but the ability to systematically translate uncertainty into sound decisions.
About our Expert
Stefan Auerbach
Expert for Supply Chain Management
Stefan Auerbach has been part of the Inventory & Supply Chain division at INFORM GmbH since 2015. As a sales specialist for AI-based software solutions, he has many years of practical experience in optimizing supply chain processes. He advises companies on analyzing their challenges, supports them in the decision-making process, and helps them implement tailored solutions for an efficient and future-proof supply chain.
