.svg)
Download Brouchure
Click the button to download the PDF file.
Download
%202.svg)
Oops! Something went wrong while submitting the form.
Traditional demand forecasting systems operate in a straightforward way: data is input, a predefined model performs calculations, and the system outputs results. In this process, the system primarily organizes data, runs models, and stores outcomes.
However, one critical capability is missing — the ability to decide which model to use.
In most cases, model selection happens in one of the following ways:
In this structure, the system can compute well, but it cannot make context-aware decisions.
In reality, demand data is highly complex. It varies significantly across SKUs, regions, channels, and time, each exhibiting different patterns. These differences directly impact the performance of forecasting models.
A model that performs well on one dataset may perform poorly on another.
Many organizations assume that building one “best” model will solve the problem. In practice, this is not the case, because each model has its own strengths.
There is no single model that is always optimal in every situation. The key is not choosing one model, but choosing the right model for each situation.
Demand forecasting can be understood as a complex system where multiple components interact. In such systems, performance depends not only on individual components but also on how they are combined and coordinated.
A complex system can be viewed as having two roles:
In demand forecasting, each model functions as a component optimized for specific conditions, and overall performance depends on how these models are selected and utilized.
Although traditional forecasting systems often include multiple models, they typically lack a dedicated mechanism to interpret data and decide which model to use.
This leads to several issues:
The core problem is not the models themselves, but the absence of a structure for selecting them.
To address this issue, the system architecture must be restructured into two layers:
This layer runs multiple forecasting models that generate demand predictions. Each model is designed to perform well under specific conditions.
This layer analyzes data and determines which model is most appropriate. It plays a central role in defining the system’s strategy.
By separating execution from decision-making, the system becomes more flexible, stable, and overall more effective.
The decision layer can be interpreted through the concept of AI agents. An agent is a system that perceives its environment and selects actions accordingly.
In a demand forecasting system:
Importantly, the agent does not perform the forecasting itself. Instead, it focuses solely on selecting the appropriate model.
This structure enables the system to adapt its forecasting strategy to diverse data environments.
This approach extends into the concept of model orchestration.
Model orchestration refers to selecting and combining multiple forecasting models based on context. Much like a conductor leads an orchestra by coordinating different instruments, the system dynamically selects and utilizes models depending on the situation.
Rather than relying on a single model, the system continuously chooses the most suitable model based on data conditions.
Demand forecasting systems are evolving from simply executing models to intelligently selecting and managing them.
In the future, success will depend less on finding a single superior model and more on the ability to identify and apply the right set of models based on the structural characteristics of demand data.
AI agent-based architectures make this shift possible, providing a foundation for stable forecasting even in complex data environments.
This concept is not just theoretical — it is already being applied in real-world industrial systems.
For example, Impactive AI’s demand forecasting solution, Deepflow, uses an AutoML-based architecture that automatically compares over 200 forecasting models. It selects the most suitable model for each item based on its data characteristics.
This approach has been effectively adopted across industries such as steel, food, and retail, helping improve forecast accuracy and reduce inventory risk.
If you are experiencing the limitations of traditional forecasting methods or exploring more robust forecasting architectures, feel free to reach out to Impactive AI.
.svg)
.svg){kind=icon}
.svg)
