The Ownership Question
Demand forecasting inside many organisations traditionally sits within supply chain or operations teams. The reasoning appears straightforward. Production planners need forecasts to plan capacity, manage procurement, and schedule manufacturing. Forecast accuracy therefore looks like a natural operational responsibility.
Yet this structure often misses a crucial perspective. Demand originates in the market rather than inside the factory.
Seeing Products the Way Customers Do
Customers rarely evaluate products using the same classification systems used by logistics teams. Warehouses organise products according to storage constraints, packaging formats, or distribution networks. Customers, by contrast, think in terms of needs, occasions, brand perception, and product substitutes.
A demand forecasting process that ignores this difference often struggles to capture real demand signals. Forecasts built purely on logistical attributes may overlook the factors that actually influence purchasing behaviour.
Marketing teams usually understand these factors more clearly. They track campaigns, promotions, product positioning, seasonal demand patterns, and shifts in customer preferences. Their perspective reflects how the market actually behaves.
Building Demand Models in SAP
When demand planning solutions are designed within platforms such as SAP, the structure of the forecasting model becomes extremely important. Most systems organise data along basic planning dimensions such as product, location, and organisational unit. These attributes provide the operational backbone of the planning system.
However, the real power of modern forecasting approaches often lies in additional attributes that describe market behaviour. Product families, brand positioning, promotional exposure, customer segments, or channel characteristics can all influence demand patterns. These attributes help forecasting models recognise relationships that are invisible within purely logistical structures.
Where Machine Learning Helps
Machine learning techniques become particularly useful in this context. Algorithms can analyse large volumes of historical demand data and identify patterns across multiple attributes simultaneously. Products that appear unrelated in logistical terms may display similar demand behaviour from a customer perspective.
When forecasting systems incorporate these market-facing attributes, machine learning models can group products according to how customers actually evaluate them. This often produces forecasting structures that align more closely with real purchasing behaviour.
The result is not simply better forecasts. It is a planning process that reflects the structure of the market itself.
The Role of Logistics
This does not mean logistics considerations disappear from demand planning. Warehouses, transportation networks, and production facilities still require forecasts organised according to operational structures. Inventory must be positioned correctly, production must be scheduled efficiently, and distribution must remain predictable.
Operational attributes therefore remain essential within planning systems.
However, the sequence matters. Forecasting models should first capture how demand behaves in the market. Once the market perspective is understood, planners can translate those insights into the logistical structures required to deliver products efficiently.
The Practical Balance
The most effective demand planning environments combine both perspectives. Marketing contributes the market-facing understanding of customer behaviour. Supply chain teams translate those insights into operational plans that production and distribution systems can execute.
When these roles are aligned properly, demand planning becomes both commercially intelligent and operationally practical.
The Larger Lesson
Forecasting is ultimately an attempt to understand human behaviour expressed through purchasing decisions. The closer a planning system moves toward that perspective, the more useful its forecasts become. Marketing teams often possess the clearest view of that behaviour.
Placing them closer to the centre of the forecasting process therefore tends to improve the quality of the demand signals that guide the entire supply chain.
