Demand forecasting is not simply a model problem. It is an operating rhythm that connects sales history, market context, supply constraints, and human planning decisions.
Choose the Right Forecasting Grain
Forecasts can be built by product, region, channel, customer segment, or inventory location. The right grain depends on the decision being made.
Avoid over-fragmented forecasts
Too much granularity can create sparse data and unstable predictions. Aggregate where the business can still act effectively.
Engineer Useful Signals
Strong forecasts often use calendar effects, promotions, holidays, pricing, inventory availability, web traffic, weather, and macro indicators.
Benchmark Before Advancing
Start with seasonal naive and moving average baselines. More complex machine learning models should beat simple methods after accounting for operational cost.
Make Forecasts Adoptable
Teams need confidence intervals, explanations, override workflows, and performance tracking. Adoption increases when planners can understand and challenge the forecast.
