Demand forecasting: methods, models and modern approaches

Demand forecasting predicts future customer demand using historical data, market signals, and statistical or machine-learning models, while demand planning converts forecasts into agreed plans and S&OP reconciles those plans with supply and capacity constraints. Forecast horizons shape the data, methods, and refresh cadence required, with short-term execution relying more heavily on recent actuals and longer-term planning requiring more judgment and external assumptions. Spreadsheet-based processes can create duplicated versions, unclear changes, and incomplete decision histories, making it difficult to assess whether planner overrides improve accuracy. Organizations commonly combine quantitative baselines, such as time-series, regression, and data-driven global models, with qualitative input for situations where historical data is insufficient, including product launches or anticipated promotions. Reliable forecasting requires governed data sources, measurable testing of assumptions and overrides, and feedback loops that record outcomes against actual demand. AI agents may support continuous monitoring by detecting deviations, rerunning forecasts at predefined thresholds, proposing explainable adjustments, and escalating exceptions for human approval. The article presents Sigma as a platform that enables forecasting, scenario write-back, audit trails, and agent-based monitoring directly on governed cloud data warehouse data.

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