Demand Planning
Definition
Demand planning is the process of predicting future customer demand and shaping it into a consensus plan that drives supply, inventory, and financial decisions. It combines statistical forecasting with market intelligence from sales, marketing, and customers.
In Practice
Demand planning starts with a statistical baseline forecast built from historical sales, then layers on human judgment: promotions, new product launches, price changes, and known customer events. The output is a single agreed demand number, usually by product, location, and month or week, that the rest of the business plans against.
Day to day, demand planners review forecast accuracy, chase down large errors, and manage exceptions rather than touching every SKU. A stable, trusted demand plan reduces firefighting downstream, because supply planners and buyers are not constantly reacting to surprises.
Consider a beverage company ahead of summer. The statistical model captures seasonality, but the planner adds uplift for a new retail listing and a planned promotion. If that uplift is wrong by 30 percent, the factory either builds pallets nobody buys or misses shelf availability during peak weeks.
Related Calculators
Related Terms
Demand forecasting is the practice of estimating future customer demand using historical data, statistical models, and market knowledge. It provides the quantitative foundation for demand planning, inventory targets, and capacity decisions.
Supply PlanningSupply planning determines how a company will meet the demand plan: what to make or buy, in what quantities, where, and when. It translates forecasted demand into production schedules, purchase plans, and inventory targets.
Sales and Operations Planning (S&OP)Sales and Operations Planning (S&OP) is a monthly cross-functional process that aligns demand, supply, inventory, and financial plans into one company-wide plan. It gives leadership a single set of numbers to run the business on over a 3-to-24-month horizon.
Demand SensingDemand sensing uses near-real-time signals such as point-of-sale data, channel inventory, orders, and external data to detect what demand is doing right now, sharpening short-term forecasts beyond what historical models can see.