Sales forecasting is mainly focused on revenue, namely estimating the total sales value or revenue per customer. It typically looks at a high level, considering things like product lines, customer segments, or regions.
Demand planning, on the other hand, goes much deeper. It doesn’t just rely on sales targets. Instead, it looks at past seasonal patterns, trends, and other factors to challenge the sales forecast and refine it. Demand planning digs into more detailed forecasts, right down to the SKU and location level.
Sales forecasting operates at a higher level, dealing with total sales value and overall volume across product lines or regions.
Demand planning takes a broader view. It doesn’t just look at what’s in the pipeline but also considers past trends and seasonality. It forecasts at a more granular level, down to the specific SKU and location.
Inputs
Sales forecasting is driven mainly by customer orders, feedback from sales teams, and market insights. It’s often shaped by the sales targets.
Demand planning pulls from a wider variety of inputs. It uses historical data, statistical models, and machine learning to predict future demand. It considers elements like seasonality, promotions, lead times, and external market conditions. In a sense, demand planning takes the sales forecast as one input among many.
Time horizon
Sales forecasting typically focuses on shorter term periods, like the next quarter or a few months ahead. Sales teams use it to set goals and manage relationships.
Demand planning has a longer view. It forecasts demand over months or even years, balancing short-term fluctuations with longer-term trends.
Constrained demand planning: After evaluating supply chain limitations, the unconstrained demand is adjusted into the constrained demand plan. This is the demand the company will aim to fulfill, based on what’s realistically achievable from the supply perspective. The constrained demand plan is then used for production planning, procurement, and inventory management.